miRNA FINGERPRINT IN THE DIAGNOSIS OF COPD

ABSTRACT

MicroRNAs (miRNA) are a recently discovered class of small non-coding RNAs (17-14 nucleotides). Due to their function as regulators of gene expression they play a critical role both in physiological and in pathological processes, such as cancer. The present invention provides novel methods for diagnosing a state of health based on the determination of specific miRNAs that have altered expression levels in different conditions, e.g. disease states compared to healthy controls.

BACKGROUND OF THE INVENTION

MicroRNAs (miRNA) are a recently discovered class of small non-coding RNAs (17-14 nucleotides). Due to their function as regulators of gene expression they play a critical role both in physiological and in pathological processes, such as cancer (Calin and Croce 2006; Esquela-Kerscher and Slack 2006; Zhang, Pan et al. 2007; Sassen, Miska et al. 2008).

There is increasing evidence that miRNAs are not only found in tissues but also in human blood both as free circulating nucleic acids and in mononuclear cells. A recent proof-of-principle study demonstrated miRNA expression pattern in pooled blood sera and pooled blood cells, both in healthy individuals and in cancer patients including patients with lung cancer (Chen, Ba et al. 2008). In addition, a remarkable stability of miRNAs in human sera was recently demonstrated (Chen, Ba et al. 2008; Gilad, Meiri et al. 2008). These findings make miRNA a potential tool for diagnostics for various types of diseases based on blood analysis.

Thus, although various markers have been proposed to indicate specific types of disorders such as prostate cancer, Wilms' tumour or COPD (Chronic obstructive pulmonary disease) there is still a need for more efficient and effective methods and compositions for the diagnosis of diseases.

SUMMARY OF THE INVENTION

The present invention provides novel methods for diagnosing diseases based on the determination of specific miRNAs that have altered expression levels in disease states compared to healthy controls or altered expression levels in a condition 1 (biological state or health state 1) compared to a condition 2 (biological state or health state 2). The disease is particularly COPD.

DEFINITIONS

miRNA

microRNAs (miRNA or pRNA) are single-stranded RNA molecules of -21-23 nucleotides in length, which regulate gene expression. miRNAs are encoded by genes from whose DNA they are transcribed but miRNAs are not translated into protein (i.e. they are non-coding RNAs). The genes encoding miRNAs are much longer than the processed mature miRNA molecule; miRNAs are first transcribed as primary transcripts or pri-miRNA with a cap and poly-A tail and processed to short, 70-nucleotide stem-loop structures known as pre-miRNA in the cell nucleus. This processing is performed in animals by a protein complex known as the Microprocessor complex, consisting of the nuclease Drosha and the double-stranded RNA binding protein Pasha. These pre-miRNAs are then processed to mature miRNAs in the cytoplasm by interaction with the endonuclease Dicer, which also initiates the formation of the RNA-induced silencing complex (RISC). When Dicer cleaves the pre-miRNA stem-loop, two complementary short RNA molecules are formed, but only one is integrated into the RISC. This strand is known as the guide strand and is selected by the argonaute protein, the catalytically active RNase in the RISC, on the basis of the stability of the 5′ end. The remaining strand, known as the miRNA*, anti-guide or passenger strand, is degraded as a RISC substrate. Therefore the miRNA*s are derived from the same hairpin structure like the “normal” miRNAs. So if the “normal” miRNA is then later called the “mature miRNA” or “guided strand”, the miRNA* is the passenger strand.

miRNA* (see also above “miRNA”)

The miRNA*s, also known as the anti-guide or passenger strand, are mostly complementary to the guide strand, but there are usually single-stranded overhangs on each end, there is usually one or a few mispairs and there are sometimes extra or missing bases causing single-stranded “bubbles. The miRNA*s are likely to act in a regulatory fashion as the miRNAs. It is understood that according to the present invention the term “miRNA” also includes the term “miRNA*”.

miRBase

A well established repository of validated miRNAs is the miRBase. The miRBase (www.mirbase.orq) is a searchable database of published miRNA sequences and annotation. Each entry in the miRBase Sequence database represents a predicted hairpin portion of a miRNA transcript (termed mir in the database), with information on the location and sequence of the mature miRNA sequence (termed miR). Both hairpin and mature sequences are available for searching and browsing, and entries can also be retrieved by name, keyword, references and annotation. All sequence and annotation data are also available for download.

miRNA-(expression) profile or miRNA fingerprint

A miRNA-Profile represents the collection of expression levels of a plurality of miRNAs, therefore it is a quantitative measure of individual miRNA expression levels. Hereby, each miRNA is represented by a numerical value.

The higher the value of an individual miRNA the higher is the expression level of this miRNA. A miRNA-profile is obtained from the RNA of a biological sample. The are various technologies to determine a miRNA-Profile, e.g. microarrays, RT-PCR, Next Generation Sequencing. As a starting material for analysis, RNA or total-RNA or any fraction thereof can be used. The plurality of miRNAs that are determined by a miRNA-profile can range from a selection of one up to all known miRNAs.

Pre-determined set of miRNAs or miRNA signature

The pre-determined set of miRNAs or miRNA signature is understood in the present invention as a fixed defined set of miRNAs which is able to differentiate between a condition 1 and another condition 2. e.g. when condition 1 is lung cancer and condition 2 is normal control, the corresponding pre-determined set of miRNAs is able to differentiate between a samples derived from a lung cancer patient or a normal control patient. Alternatively, condition 1 is lung cancer and condition 2 is multiple sclerosis, the corresponding pre-determined set of miRNAs is able to differentiate between a lung cancer patient and a multiple sclerosis patient. In order to be able to perform the sample analysis it is required that, e.g. on the matrix that will be used to determine a miRNA profile, these fixed defined set of miRNAs have to be represented by capture probes that are defined by the pre-determined set of miRNAs. For example, when the predetermined set of miRNAs for diagnosing lung cancer from healthy controls consists of 25 miRNAs, probes capable for detecting these 25 miRNAs have to be implemented for performing the diagnostic analysis.

Common miRNA Signature Profile

A common miRNA signature profile is understood in the present invention as a non-fixed defined set of miRNAs or non-coding RNAs which is able to differentiate between a condition 1 and another condition 2. The common miRNA or non-coding RNA signature profile is calculated “on-the-fly” from a plurality of miRNA-profiles that are stored, e.g. in database. The common miRNA signature profile which is able to differentiate between a condition 1 and another condition 2 is changing as soon as an new profile is added to the database which is relevant to either to state of health 1 or another condition 2. In this respect it is different from a predetermined set of miRNAs (see above). Furthermore, the basis for generating the common miRNA signature profile—hence the miRNA profiles stored in the database—is generated from capture probes, e.g. on a matrix that is representing as much as possible different capture probes for detecting as much as possible, ideally all known, miRNAs.

Non-Coding RNA

A non-coding RNA (ncRNA) is a functional RNA molecule that is not translated into a protein. Less-frequently used synonyms are non-protein-coding RNA (npcRNA), non-messenger RNA (nmRNA), small non-messenger RNA (snmRNA), functional RNA (fRNA). The term small RNA (sRNA) is often used for bacterial ncRNAs. The DNA sequence from which a non-coding RNA is transcribed as the end product is often called an RNA gene or non-coding RNA gene.

Non-coding RNA genes include highly abundant and functionally important RNAs such as transfer RNA (tRNA) and ribosomal RNA (rRNA), as well as RNAs such as snoRNAs, microRNAs, siRNAs and piRNAs and the long ncRNAs that include examples such as Xist and HOTAIR (see here for a more complete list of ncRNAs). The number of ncRNAs encoded within the human genome is unknown, however recent transcriptomic and bioinformatic studies suggest the existence of thousands of ncRNAs. Since most of the newly identified ncRNAs have not been validated for their function, it is possible that many are non-functional.

Condition

A condition (biological state or health state or state of health) is understood in the present invention as status of a subject that can be described by physical, mental or social criteria. It includes as well so-called “healthy” and “diseased” conditions, therefore it is not limited to the WHO definition of health as “a state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity.” but includes disease and infirmity. For the definition of diseases comprised, e.g. by the conditions of the present invention, it is referred to the international classification of diseases (ICD) of the WHO (http://www.who.int/classifications/icd/en /index.html). When 2 or more conditions are compared according to the present invention, it is understood that this is possible for all conditions that can be defined and is not limited to a comparison of a disease versus healthy and extends to multi-way comparisons. Examples for comparison are, but not limited to:

pairwise comparisons:

-   -   lung cancer vs. healthy control, pancreatic cancer vs. healthy         control     -   lung cancer vs. pancreatic cancer, lung cancer vs. multiple         sclerosis     -   lung cancer WHO grade 1 vs. lung cancer WHO grade 2     -   lung cancer WHO grade 1 metastasing vs. lung cancer WHO grade 1         non-metastasing     -   Morbus Crohn vs. collitis     -   Pancreatic cancer vs. pancreatitis

multi-way comparisons :

-   -   Lung cancer vs. pancreatic cancer vs. multiple sclerosis     -   Pancreas cancer vs. pancreatitis vs. lung cancer WHO grade 1         non-metastasing

COPD (Chronic Obstructive Pulmonary Disease)

Chronic obstructive pulmonary disease (COPD) refers to chronic bronchitis and emphysema, a pair of two commonly co-existing diseases of the lungs in which the airways become narrowed. This leads to a limitation of the flow of air to and from the lungs causing shortness of breath. In contrast to asthma, the limitation of airflow is poorly reversible and usually gets progressively worse over time.

COPD is caused by noxious particles or gas, most commonly from tobacco smoking, which triggers an abnormal inflammatory response in the lung. The inflammatory response in the larger airways is known as chronic bronchitis, which is diagnosed clinically when people regularly cough up sputum. In the alveoli, the inflammatory response causes destruction of the tissues of the lung, a process known as emphysema. The natural course of COPD is characterized by occasional sudden worsenings of symptoms called acute exacerbations, most of which are caused by infections or air pollution.

The diagnosis of COPD requires lung function tests. Important management strategies are smoking cessation, vaccinations, rehabilitation, and drug therapy (often using inhalers). Some patients go on to requiring long-term oxygen therapy or lung transplantation.

Worldwide, COPD ranked as the sixth leading cause of death in 1990. It is projected to be the fourth leading cause of death worldwide by 2030 due to an increase in smoking rates and demographic changes in many countries. COPD is the 4th leading cause of death in the U.S., and the economic burden of COPD in the U.S. in 2007 was $42.6 billion in health care costs and lost productivity.

COPD is also known as chronic obstructive lung disease (COLD), chronic obstructive airway disease (COAD), chronic airflow limitation (CAL) and chronic obstructive respiratory disease (CORD).

A “biological sample” in terms of the invention means a sample of biological tissue or fluid. Examples of biological samples are sections of tissues, blood, blood fractions, plasma, serum, urine or samples from other peripheral sources. or cell cultures, cell colonies of even single cells, or a collection of single cells. Furthermore, also pools or mixture of the above mentioned samples may be employed. A biological sample may be provided by removing a sample of cells from a subject, but can also be provided by using a previously isolated sample. For example, a tissue sample can be removed from a subject suspected of having a disease by conventional biopsy techniques. In a preferred embodiment, a blood sample is taken from the subject. In one embodiment, the blood or tissue sample is obtained from the subject prior to initiation of radiotherapy, chemotherapy or other therapeutic treatment. According to the invention, the biological sample preferably is a blood or a serum sample. Further, it is also preferred to use blood cells, e.g. erythrocytes, leukocytes or thrombocytes.

A biological sample from a patient means a sample from a subject suspected to be affected by a disease. As used herein, the term “subject” refers to any mammal, including both human and other mammals. Preferably, the methods of the present invention are applied to human subjects.

Subject-matter of the invention is a method for diagnosing a disease, comprising the steps

-   (a) determining an expression profile of a predetermined set of     miRNAs in a biological sample from a patient (or subject); and -   (b) comparing said expression profile to a reference expression     profile, wherein the comparison of said determined expression     profile to said reference expression profile allows for the     diagnosis of the disease.

In step (a) of the above method of the invention, an expression profile of a predetermined set of miRNAs is determined. The determination may be carried out by any convenient means for determining nucleic acids. For expression profiling, qualitative, semi-quantitative and preferably quantitative detection methods can be used. A variety of techniques are well known to those of skill in the art. In particular, the determination may comprise nucleic acid hybridization and/or nucleic acid amplification steps.

Nucleic acid hybridization may for example be performed using a solid phase nucleic acid biochip array, in particular a microarray, or in situ hybridization. The miRNA microarray technology affords the analysis of a complex biological sample for all expressed miRNAs. Nucleotides with complementarity to the corresponding miRNAs are spotted on coated carriers or are fabricated by in-situ synthesis methods on a carrier. Preferably, miRNAs isolated from the sample of interest are not labelled, e.g. before hybridization of the miRNAs to the complementary sequences on the carrier and the resulting signal indicating the occurrence of a distinct miRNA is generated by incorporation of a detectable label (e.g. biotin, fluorescent dye) by means of an enzyme reaction.

According to another embodiment of the invention, miRNAs isolated from the sample of interest are labelled, e.g. fluorescently labelled, so that upon hybridization of the miRNAs to the complementary sequences on the carrier the resulting signal indicates the occurrence of a distinct miRNA. On one miRNA microarray, preferably at least the whole predetermined set of miRNAs can be analyzed.

Further, quantitative real-time polymerase chain reaction (RT-PCR) can be used to detect miRNAs even at very low abundance.

Alternative methods for obtaining expression profiles may also contain sequencing, next generation sequencing or mass spectroscopy.

The predetermined set of miRNAs in step (a) of the above method of the invention depends on the disease to be diagnosed. The inventors found out that single miRNA biomarkers lack sufficient accuracy, specificity and sensitivity, and therefore it is preferred to analyze more complex miRNA expression patterns, so-called miRNA signatures. The predetermined set of miRNAs comprises one or more, preferably a larger number of miRNAs (miRNA signatures) that are differentially regulated in samples of a patient affected by a particular disease compared to healthy controls. Alternatively, the disease can also be compared to any other defined condition (e.g. another disease).

The expression profile determined in the above step (a) is subsequently compared to a reference expression profile or to a plurality of reference profiles in the above step (b). The reference expression profile is the expression profile of the same set of miRNAs in a biological sample originating from the same source as the biological sample from a patient but obtained from a healthy subject. Preferably, both the reference expression profile and the expression profile of the above step (a) are determined in a blood or serum sample or in a sample of erythrocytes, leukocytes and/or thrombocytes. It is understood that the reference expression profile is not necessarily obtained from a single healthy subject but may be an average expression profile of a plurality of healthy subjects. It is preferred to use a reference expression profile obtained from a person of the same gender, and a similar age as the patient.

The above method of the invention is suitable for diagnosing any diseases for which a differential expression of miRNAs compared to healthy controls or other diseases exists. In particular, the method may be used for diagnosing cancer including bladder cancer, brain cancer, breast cancer, colon cancer, endometrium cancer, gastrointestinal stromal cancer, glioma, head- and neck cancer, kidney cancer, leukemia, liver cancer, lung cancer, lymph node cancer, melanoma, meninges cancer, ovarian cancer, pancreas cancer, prostate cancer, sarcoma, stomach cancer, testicular cancer, thyroid cancer, thymus cancer and Wilms' tumour or COPD. The diagnosis may comprise determining type, rate and/or stage of cancer. The course of the disease and the success of therapy such as chemotherapy may be monitored. The method of the invention provides a prognosis on the survivor rate and enables to determine a patient's response to drugs.

In addition to cancer, also different types of diseases may be diagnosed by means of the above method of the invention, if the disease state is correlated with a differential expression of miRNAs compared to a healthy control. For example the disease may be Alzheimer's disease, multiple sclerosis, melanoma, Morbus Crohn and cardiovascular diseases. The inventors found out that also these diseases are correlated with a specific expression profile of miRNAs.

The inventors succeeded in developing a generally applicable approach to arrive at miRNA signatures that are correlated with a particular disease. In more detail, the following steps are accomplished:

-   1. miRNAs are extracted from a biological sample of a patient,     preferably a blood or serum or urine sample or a sample comprising     erythrocytes, leukocytes or thrombocytes, using suitable     kits/purification methods. From these samples preferably the     RNA-fraction is used for analysis. -   2. The respective samples are measured using experimental     techniques.

These techniques include but are not restricted to:

-   -   Array based approaches     -   Real time quantitative polymerase chain reaction     -   Sequencing     -   Next Generation Sequencing     -   Mass Spectroscopy

-   3. Mathematical approaches are applied to gather information on the     value and the redundancy of single biomarkers. These methods     include, but are not restricted to:     -   basic mathematic approaches (e.g. Fold Quotients, Signal to         Noise ratios, Correlation)     -   statistical methods as hypothesis tests (e.g. t-test,         Wilcoxon-Mann-Whitney test), the Area under the Receiver         operator Characteristics Curve     -   Information Theory approaches, (e.g. the Mutual Information,         Cross-entropy)     -   Probability theory (e.g. joint and conditional probabilities)     -   Combinations and modifications of the previously mentioned         examples

-   4. The information collected in 3) are used to estimate for each     biomarker the diagnostic content or value. Usually, however, this     diagnostic value of only one biomarker is too small to get a highly     accurate diagnosis with accuracy rates, specificities and     sensitivities beyond the 90% barrier. Please note that the     diagnostic content for our miRNAs can be found in the tables in     FIGS. 3 and 5. These tables includes the miRNAs with the sequences,     and the significance value as computed by a t-test and further     statistical measures.

-   5. Thus statistical learning/machine     learning/bioinformatics/computational approaches are applied to     define subsets of biomarkers that are tailored for the detection of     diseases. These techniques includes but are not restricted to     -   Wrapper subset selection techniques (e.g. forward step-wise,         backward step-wise, combinatorial approaches, optimization         approaches)     -   Filter subset selection methods (e.g. the methods mentioned in         3)     -   Principal Component Analysis     -   Combinations and modifications of such methods (e.g. hybrid         approaches)

-   6. The diagnostic content of each detected set can be estimated by     mathematical and/or computational techniques to define the     diagnostic information content of subsets.

-   7. The subsets, detected in step 5, which may range from only a     small number (at least two) to all measured biomarkers is then used     to carry out a diagnosis. To this end, statistical learning/machine     learning/bioinformatics/computational approaches are applied that     include but are not restricted to any type of supervised or     unsupervised analysis:     -   Classification techniques (e.g. naïve Bayes, Linear Discriminant         Analysis, Quadratic Discriminant Analysis Neural Nets, Tree         based approaches, Support Vector Machines, Nearest Neighbour         Approaches)     -   Regression techniques (e.g. linear Regression, Multiple         Regression, logistic regression, probit regression, ordinal         logistic regression ordinal Probit-Regression, Poisson         Regression, negative binomial Regression, multinomial logistic         Regression, truncated regression)     -   Clustering techniques (e.g. k-means clustering, hierarchical         clustering, PCA)     -   Adaptations, extensions, and combinations of the previously         mentioned approaches

The inventors surprisingly found out that the described approach yields in miRNA signatures that provide high diagnostic accuracy, specificity and sensitivity in the determination of diseases.

According to a preferred embodiment of the invention, the disease to be determined is COPD.

The inventors succeeded in determining miRNAs that are differentially regulated in samples from COPD patients as compared to healthy controls or lung cancer patients. A complete overview of all miRNAs that are found to be differentially regulated in blood samples of COPD patients is provided in the tables shown in FIGS. 3 and 5.

In the tables shown in FIGS. 3 and 5, the miRNAs that are found to be differentially regulated are sorted in the order of their t-test significance. Another method for assessing the significance is to compute the Mutual information (MI) (Shannon, 1984) which is an adequate measure to estimate the overall diagnostic information content of single biomarkers (Keller, Ludwig et al., 2006). According to the invention mutual information is considered as the reduction in uncertainty about the class labels. “0” for controls and “1” for COPD samples due to the knowledge of the miRNA expression. The higher the value of the MI of a miRNA, the higher is the diagnostic content of the respective miRNA.

Diagnosis of COPD

According to a preferred embodiment of the invention, the disease to be determined is COPD. Surprisingly, the inventors found out that miRNAs are differentially regulated in samples from COPD patients as compared to healthy controls. In total, 81 miRNAs were found to be significantly deregulated (t-test significance <0.05) in blood cells of COPD patients as compared to the healthy controls.

Thus, preferably the predetermined set of miRNAs for the diagnosis of COPD comprises one or more nucleic acids selected from the 81 most deregulated miRNAs.

Preferably, the predetermined set of miRNAs comprises at least 7, preferably at least 10, 15, 20, 25, 30, 35, 40, 50, 75, 100 or all of the above-indicated nucleic acids.

In a further embodiment the predetermined set of miRNAs for the diagnosis of COPD comprises one or more miRNAs selected from the group consisting of hsa-miR-182*, hsa-miR-205, hsa-miR-662, hsa-miR-636, hsa-miR-554, hsa-miR-1233, hsa-miR-330-3p.

In a further embodiment the predetermined set of miRNAs for the diagnosis of COPD comprises one or more miRNAs selected from the group consisting of hsa-miR-182*, hsa-miR-205, hsa-miR-662, hsa-miR-636, hsa-miR-554, hsa-miR-1233, hsa-miR-330-3p, hsa-miR-492, hsa-miR-383, hsa-miR-518b.

In a further embodiment the predetermined set of miRNAs for the diagnosis of COPD comprises one or more miRNAs selected from the group consisting of hsa-miR-182*, hsa-miR-205, hsa-miR-662, hsa-miR-636, hsa-miR-554, hsa-miR-1233, hsa-miR-330-3p, hsa-miR-492, hsa-miR-383, hsa-miR-518b, hsa-miR-23b, hsa-miR-188-5p, hsa-miR-216a, hsa-miR-625*, hsa-miR-26a.

In a further embodiment the predetermined set of miRNAs for the diagnosis of COPD comprises one or more miRNAs selected from the group consisting of hsa-miR-182*, hsa-miR-205, hsa-miR-662, hsa-miR-636, hsa-miR-554, hsa-miR-1233, hsa-miR-330-3p, hsa-miR-492, hsa-miR-383, hsa-miR-518b, hsa-miR-23b, hsa-miR-188-5p, hsa-miR-216a, hsa-miR-625*, hsa-miR-26a, hsa-miR-30c, hsa-miR-200a*, hsa-miR-24-1*, hsa-miR-641, hsa-miR-32.

In a further embodiment the predetermined set of miRNAs for the diagnosis of COPD comprises one or more miRNAs selected from the group consisting of hsa-miR-182*, hsa-miR-205, hsa-miR-662, hsa-miR-636, hsa-miR-554, hsa-miR-1233, hsa-miR-330-3p, hsa-miR-492, hsa-miR-383, hsa-miR-518b, hsa-miR-23b, hsa-miR-188-5p, hsa-miR-216a, hsa-miR-625*, hsa-miR-26a, hsa-miR-30c, hsa-miR-200a*, hsa-miR-24-1*, hsa-miR-641, hsa-miR-32, hsa-miR-646, hsa-miR-92a, hsa-miR-566, hsa-miR-1291, hsa-miR-23a.

In a further embodiment the predetermined set of miRNAs for the diagnosis of COPD comprises one or more miRNAs selected from the group consisting of hsa-miR-182*, hsa-miR-205, hsa-miR-662, hsa-miR-636, hsa-miR-554, hsa-miR-1233, hsa-miR-330-3p, hsa-miR-492, hsa-miR-383, hsa-miR-518b, hsa-miR-23b, hsa-miR-188-5p, hsa-miR-216a, hsa-miR-625*, hsa-miR-26a, hsa-miR-30c, hsa-miR-200a*, hsa-miR-24-1*, hsa-miR-641, hsa-miR-32, hsa-miR-646, hsa-miR-92a, hsa-miR-566, hsa-miR-1291, hsa-miR-23a, hsa-miR-24-2*, hsa-miR-548p, hsa-miR-1470, hsa-miR-199a-5p, hsa-miR-25.

In a further embodiment the predetermined set of miRNAs for the diagnosis of COPD comprises one or more miRNAs selected from the group consisting of hsa-miR-182*, hsa-miR-205, hsa-miR-662, hsa-miR-636, hsa-miR-554, hsa-miR-1233, hsa-miR-330-3p, hsa-miR-492, hsa-miR-383, hsa-miR-518b, hsa-miR-23b, hsa-miR-188-5p, hsa-miR-216a, hsa-miR-625*, hsa-miR-26a, hsa-miR-30c, hsa-miR-200a*, hsa-miR-24-1*, hsa-miR-641, hsa-miR-32, hsa-miR-646, hsa-miR-92a, hsa-miR-566, hsa-miR-1291, hsa-miR-23a, hsa-miR-24-2*, hsa-miR-548p, hsa-miR-1470, hsa-miR-199a-5p, hsa-miR-25, hsa-miR-136, hsa-miR-342-5p, hsa-miR-1229, hsa-miR-369-5p, hsa-miR-634.

In a further embodiment the predetermined set of miRNAs for the diagnosis of COPD comprises one or more miRNAs selected from the group consisting of hsa-miR-182*, hsa-miR-205, hsa-miR-662, hsa-miR-636, hsa-miR-554, hsa-miR-1233, hsa-miR-330-3p, hsa-miR-492, hsa-miR-383, hsa-miR-518b, hsa-miR-23b, hsa-miR-188-5p, hsa-miR-216a, hsa-miR-625*, hsa-miR-26a, hsa-miR-30c, hsa-miR-200a*, hsa-miR-24-1*, hsa-miR-641, hsa-miR-32, hsa-miR-646, hsa-miR-92a, hsa-miR-566, hsa-miR-1291, hsa-miR-23a, hsa-miR-24-2*, hsa-miR-548p, hsa-miR-1470, hsa-miR-199a-5p, hsa-miR-25, hsa-miR-136, hsa-miR-342-5p, hsa-miR-1229, hsa-miR-369-5p, hsa-miR-634, hsa-miR-1913, hsa-miR-548o, hsa-miR-183, hsa-miR-182, hsa-miR-129-3p.

In a further embodiment the predetermined set of miRNAs for the diagnosis of COPD comprises one or more miRNAs selected from the group consisting of hsa-miR-182*, hsa-miR-205, hsa-miR-662, hsa-miR-636, hsa-miR-554, hsa-miR-1233, hsa-miR-330-3p, hsa-miR-492, hsa-miR-383, hsa-miR-518b, hsa-miR-23b, hsa-miR-188-5p, hsa-miR-216a, hsa-miR-625*, hsa-miR-26a, hsa-miR-30c, hsa-miR-200a*, hsa-miR-24-1*, hsa-miR-641, hsa-miR-32, hsa-miR-646, hsa-miR-92a, hsa-miR-566, hsa-miR-1291, hsa-miR-23a, hsa-miR-24-2*, hsa-miR-548p, hsa-miR-1470, hsa-miR-199a-5p, hsa-miR-25, hsa-miR-136, hsa-miR-342-5p, hsa-miR-1229, hsa-miR-369-5p, hsa-miR-634, hsa-miR-1913, hsa-miR-548o, hsa-miR-183, hsa-miR-182, hsa-miR-129-3p, hsa-miR-500*, hsa-miR-1308, hsa-miR-1471, hsa-miR-195, hsa-miR-361-5p, hsa-miR-515-5p, hsa-miR-224, hsa-miR-151-5p, hsa-miR-564, hsa-miR-934.

In a further embodiment the predetermined set of miRNAs for the diagnosis of COPD comprises one or more miRNAs selected from the group consisting of hsa-miR-182*, hsa-miR-205, hsa-miR-662, hsa-miR-636, hsa-miR-554, hsa-miR-1233, hsa-miR-330-3p, hsa-miR-492, hsa-miR-383, hsa-miR-518b, hsa-miR-23b, hsa-miR-188-5p, hsa-miR-216a, hsa-miR-625*, hsa-miR-26a, hsa-miR-30c, hsa-miR-200a*, hsa-miR-24-1*, hsa-miR-641, hsa-miR-32, hsa-miR-646, hsa-miR-92a, hsa-miR-566, hsa-miR-1291, hsa-miR-23a, hsa-miR-24-2*, hsa-miR-548p, hsa-miR-1470, hsa-miR-199a-5p, hsa-miR-25, hsa-miR-136, hsa-miR-342-5p, hsa-miR-1229, hsa-miR-369-5p, hsa-miR-634, hsa-miR-1913, hsa-miR-548o, hsa-miR-183, hsa-miR-182, hsa-miR-129-3p, hsa-miR-500*, hsa-miR-1308, hsa-miR-1471, hsa-miR-195, hsa-miR-361-5p, hsa-miR-515-5p, hsa-miR-224, hsa-miR-151-5p, hsa-miR-564, hsa-miR-934, hsa-miR-146b-5p, hsa-miR-214, hsa-miR-216b, hsa-miR-28-5p, hsa-miR-940, hsa-miR-606, hsa-miR-631, hsa-miR-21*, hsa-miR-384, hsa-miR-1234, hsa-miR-1260, hsa-miR-532-3p, hsa-miR-122*, hsa-miR-199a-3p, hsa-miR-33b*, hsa-miR-184, hsa-miR-373*, hsa-miR-145, hsa-miR-27a, hsa-miR-1258, hsa-miR-124, hsa-miR-489, hsa-miR-559, hsa-miR-223.

Most preferably, the predetermined set of miRNAs comprises those miRNAs that were most significantly deregulated.

The predetermined set of miRNAs for the diagnosis of COPD should preferably comprise at least 7, preferably at least 10, 15, 20, 25, 30, 35, 40, 50, 75, 100, preferably all of the known miRNAs, preferably all of the 863 (see FIG. 1, representing the current status of all known miRNAs in the version 12, 13, and 14 of the miRBase repository (www.mirbase.org).

Another embodiment of the present invention is a kit for diagnosing a disease, comprising means for determining an expression profile of a predetermined set of miRNAs in a biological sample, in particular in a blood and/or serum sample. Preferably, one or more reference expression profiles are also provided which show the expression profile of the same set of miRNAs in the same type of biological sample, in particular in a blood and/or serum sample, obtained from one or more healthy subjects. A comparison to said reference expression profile(s) allows for the diagnosis of the disease.

Another preferred embodiment of the present invention is a kit for diagnosing COPD, comprising means for determining the expression profile of one or more milRNA selected from the group consisting of hsa-miR-182*, hsa-miR-205, hsa-miR-662, hsa-miR-636, hsa-miR-554, hsa-miR-1233, hsa-miR-330-3p, hsa-miR-492, hsa-miR-383, hsa-miR-518b, hsa-miR-23b, hsa-miR-188-5p, hsa-miR-216a, hsa-miR-625*, hsa-miR-26a, hsa-miR-30c, hsa-miR-200a*, hsa-miR-24-1 *, hsa-miR-641, hsa-miR-32, hsa-miR-646, hsa-miR-92a, hsa-miR-566, hsa-miR-1291, hsa-miR-23a, hsa-miR-24-2*, hsa-miR-548p, hsa-miR-1470, hsa-miR-199a-5p, hsa-miR-25, hsa-miR-136, hsa-miR-342-5p, hsa-miR-1229, hsa-miR-369-5p, hsa-miR-634, hsa-miR-1913, hsa-miR-548o, hsa-miR-183, hsa-miR-182, hsa-miR-129-3p, hsa-miR-500*, hsa-miR-1308, hsa-miR-1471, hsa-miR-195, hsa-miR-361-5p, hsa-miR-515-5p, hsa-miR-224, hsa-miR-151-5p, hsa-miR-564, hsa-miR-934.

In a preferred embodiment the kit comprises means for determining at least 7, preferably at least 10, 15, 20, 25, 30, 35, 40, 50 ,75, 100 or all of the indicated miRNAs. The kit for diagnosing COPD is particularly suitable for diagnosing COPD in a blood and/or serum sample or in a sample comprising erythrocytes, leukocytes and/or thrombocytes.

The means for determining a predetermined set of miRNAs may for example comprise a microarray comprising miRNA-specific oligonucleotide probes. In a preferred embodiment, the microarray comprises miRNA-specific oligonucleotide probes for the detection of miRNAs. Depending on the intended use of the microarray in the diagnosis or prognosis of a particular disease, probes for detecting different miRNAs may be included.

A microarray intended for use in the diagnosis of COPD preferably comprises miRNA specific oligonucleotide probes for one or more miRNA selected from the group consisting of hsa-miR-182*, hsa-miR-205, hsa-miR-662, hsa-miR-636, hsa-miR-554, hsa-miR-1233, hsa-miR-330-3p, hsa-miR-492, hsa-miR-383, hsa-miR-518b, hsa-miR-23b, hsa-miR-188-5p, hsa-miR-216a, hsa-miR-625*, hsa-miR-26a, hsa-miR-30c, hsa-miR-200a*, hsa-miR-24-1*, hsa-miR-641, hsa-miR-32, hsa-miR-646, hsa-miR-92a, hsa-miR-566, hsa-miR-1291, hsa-miR-23a, hsa-miR-24-2*, hsa-miR-548p, hsa-miR-1470, hsa-miR-199a-5p, hsa-miR-25, hsa-miR-136, hsa-miR-342-5p, hsa-miR-1229, hsa-miR-369-5p, hsa-miR-634, hsa-miR-1913, hsa-miR-548o, hsa-miR-183, hsa-miR-182, hsa-miR-129-3p, hsa-miR-500*, hsa-miR-1308, hsa-miR-1471, hsa-miR-195, hsa-miR-361-5p, hsa-miR-515-5p, hsa-miR-224, hsa-miR-151-5p, hsa-miR-564, hsa-miR-934.

In a preferred embodiment the microarray comprises oligonucleotide probes for determining at least 7, preferably at least 10, 15, 20, 25, 30, 35, 40, 50, 75, 100 or all of the indicated miRNAs.

The microarray can comprise oligonucleotide probes obtained from known or predicted miRNA sequences. The array may contain different oligonucleotide probes for each miRNA, for example one containing the active mature sequence and another being specific for the precursor of the miRNA. The array may also contain controls such as one or more sequences differing from the human orthologs by only a few bases, which can serve as controls for hybridization stringency conditions. It is also possible to include viral miRNAs or putative miRNAs as predicted from bioinformatic tools. Further, it is possible to include appropriate controls for non-specific hybridization on the microarray.

Differential Diagnosis of Lung Cancer Patients and COPD

According to another preferred embodiment of the invention, the diseases to be diagnosed or differentiated is COPD from lung cancer. Surprisingly, the inventors found out that miRNAs are differentially regulated in samples from COPD patients as compared to lung cancer patients. In total, 86 miRNAs were found to be significantly deregulated (t-test significance <0.05) in blood cells of COPD patients as compared to the lung cancer patients.

The predetermined set of miRNAs should preferably comprise at least 1, preferably at least 7, 10, 15 ,20, 25, 30, 35, 40, 50, 75 or 100 of the indicated nucleic acids.

Thus, preferably the predetermined set of miRNAs for the diagnosis/differentiation of COPD from lung cancer comprises one or more nucleic acids selected from the 86 most deregulated miRNAs.

Preferably, the predetermined set of miRNAs comprises at least 7, preferably at least 10, 15, 20, 25, 30, 35, 40, 50, 75, 100 or all of the above-indicated nucleic acids.

In a further embodiment the predetermined set of miRNAs for the diagnosis/differentiation of COPD from lung cancer comprises one or more miRNAs selected from the group consisting of hsa-miR-369-5p, hsa-miR-662, hsa-miR-636, hsa-miR-26a, hsa-miR-383, hsa-miR-92a, hsa-miR-328.

In a further embodiment the predetermined set of miRNAs for the diagnosis/differentiation of COPD from lung cancer comprises one or more miRNAs selected from the group consisting of hsa-miR-369-5p, hsa-miR-662, hsa-miR-636, hsa-miR-26a, hsa-miR-383, hsa-miR-92a, hsa-miR-328, hsa-miR-675, hsa-rniR-1224-3p, hsa-let-7d*.

In a further embodiment the predetermined set of miRNAs for the diagnosis/differentiation of COPD from lung cancer comprises one or more miRNAs selected from the group consisting of hsa-miR-369-5p, hsa-miR-662, hsa-miR-636, hsa-miR-26a, hsa-miR-383, hsa-miR-92a, hsa-miR-328, hsa-miR-675, hsa-miR-1224-3p, hsa-let-7d*, hsa-miR-641, hsa-miR-25, hsa-miR-93*, hsa-miR-940, hsa-miR-631.

In a further embodiment the predetermined set of miRNAs for the diagnosis/differentiation of COPD from lung cancer comprises one or more miRNAs selected from the group consisting of hsa-miR-369-5p, hsa-miR-662, hsa-miR-636, hsa-miR-26a, hsa-miR-383, hsa-miR-92a, hsa-miR-328, hsa-miR-675, hsa-miR-1224-3p, hsa-let-7d*, hsa-miR-641, hsa-miR-25, hsa-miR-93*, hsa-miR-940, hsa-miR-631, hsa-miR-513b, hsa-miR-130b, hsa-miR-30e*, hsa-miR-193a-3p, hsa-miR-323-3p.

In a further embodiment the predetermined set of miRNAs for the diagnosis/differentiation of COPD from lung cancer comprises one or more miRNAs selected from the group consisting of hsa-miR-369-5p, hsa-miR-662, hsa-miR-636, hsa-miR-26a, hsa-miR-383, hsa-miR-92a, hsa-miR-328, hsa-miR-675, hsa-miR-1224-3p, hsa-let-7d*, hsa-miR-641, hsa-miR-25, hsa-miR-93*, hsa-miR-940, hsa-miR-631, hsa-miR-513b, hsa-miR-130b, hsa-miR-30e*, hsa-miR-193a-3p, hsa-miR-323-3p, hsa-miR-634, hsa-miR-224, hsa-miR-200a*, hsa-miR-205, hsa-miR-363.

In a further embodiment the predetermined set of miRNAs for the diagnosis/differentiation of COPD from lung cancer comprises one or more miRNAs selected from the group consisting of hsa-miR-369-5p, hsa-miR-662, hsa-miR-636, hsa-miR-26a, hsa-miR-383, hsa-miR-92a, hsa-miR-328, hsa-miR-675, hsa-miR-1224-3p, hsa-let-7d*, hsa-miR-641, hsa-miR-25, hsa-miR-93*, hsa-miR-940, hsa-miR-631, hsa-miR-513b, hsa-miR-130b, hsa-miR-30e*, hsa-miR-193a-3p, hsa-miR-323-3p, hsa-miR-634, hsa-miR-224, hsa-miR-200a*, hsa-miR-205, hsa-miR-363, hsa-miR-877, hsa-miR-1229, hsa-miR-1308, hsa-miR-1911, hsa-miR-376a*.

In a further embodiment the predetermined set of miRNAs for the diagnosis/differentiation of COPD from lung cancer comprises one or more miRNAs selected from the group consisting of hsa-miR-369-5p, hsa-miR-662, hsa-miR-636, hsa-miR-26a, hsa-miR-383, hsa-miR-92a, hsa-miR-328, hsa-miR-675, hsa-miR-1224-3p, hsa-let-7d*, hsa-miR-641, hsa-miR-25, hsa-miR-93*, hsa-miR-940, hsa-miR-631, hsa-miR-513b, hsa-miR-130b, hsa-miR-30e*, hsa-miR-193a-3p, hsa-miR-323-3p, hsa-miR-634, hsa-miR-224, hsa-miR-200a*, hsa-miR-205, hsa-miR-363, hsa-miR-877, hsa-miR-1229, hsa-miR-1308, hsa-miR-1911, hsa-miR-376a*, hsa-miR-183, hsa-miR-199a-3p, hsa-miR-1291, hsa-miR-184, hsa-miR-139-5p.

In a further embodiment the predetermined set of miRNAs for the diagnosis/differentiation of COPD from lung cancer comprises one or more miRNAs selected from the group consisting of hsa-miR-369-5p, hsa-miR-662, hsa-miR-636, hsa-miR-26a, hsa-miR-383, hsa-miR-92a, hsa-miR-328, hsa-miR-675, hsa-miR-1224-3p, hsa-let-7d*, hsa-miR-641, hsa-miR-25, hsa-miR-93*, hsa-miR-940, hsa-miR-631, hsa-miR-513b, hsa-miR-130b, hsa-miR-30e*, hsa-miR-193a-3p, hsa-miR-323-3p, hsa-miR-634, hsa-miR-224, hsa-miR-200a*, hsa-miR-205, hsa-miR-363, hsa-miR-877, hsa-miR-1229, hsa-miR-1308, hsa-miR-1911, hsa-miR-376a*, hsa-miR-183, hsa-miR-199a-3p, hsa-miR-1291, hsa-miR-184, hsa-miR-139-5p, hsa-miR-550, hsa-miR-657, hsa-miR-1258, hsa-miR-874, hsa-miR-626.

In a further embodiment the predetermined set of miRNAs for the diagnosis/differentiation of COPD from lung cancer comprises one or more miRNAs selected from the group consisting of hsa-miR-369-5p, hsa-miR-662, hsa-miR-636, hsa-miR-26a, hsa-miR-383, hsa-miR-92a, hsa-miR-328, hsa-miR-675, hsa-miR-1224-3p, hsa-let-7d*, hsa-miR-641, hsa-miR-25, hsa-miR-93*, hsa-miR-940, hsa-miR-631, hsa-miR-513b, hsa-miR-130b, hsa-miR-30e*, hsa-miR-193a-3p, hsa-miR-323-3p, hsa-miR-634, hsa-miR-224, hsa-miR-200a*, hsa-miR-205, hsa-miR-363, hsa-miR-877, hsa-miR-1229, hsa-miR-1308, hsa-miR-1911, hsa-miR-376a*, hsa-miR-183, hsa-miR-199a-3p, hsa-miR-1291, hsa-miR-184, hsa-miR-139-5p, hsa-miR-550, hsa-miR-657, hsa-miR-1258, hsa-miR-874, hsa-miR-626, hsa-miR-483-3p, hsa-miR-1260, hsa-miR-1827, hsa-miR-95, hsa-miR-487a, hsa-miR-1271, hsa-miR-126, hsa-miR-1233, hsa-miR-559, hsa-miR-515-3p.

Most preferably, the predetermined set of miRNAs comprises those miRNAs that were most significantly deregulated.

The predetermined set of miRNAs for the diagnosis/differentiation of COPD from lung cancer should preferably comprise at least 7, preferably at least 10, 15, 20, 25, 30, 35, 40, 50, 75, 100, preferably all of the known miRNAs, preferably all of the 863 (see FIG. 1, representing the current status of all known miRNAs in the version 12, 13, and 14 of the miRBase repository (www.mirbase.orq).

Another embodiment of the present invention is a kit for diagnosing a disease, comprising means for determining an expression profile of a predetermined set of miRNAs in a biological sample, in particular in a blood and/or serum sample. Preferably, one or more reference expression profiles are also provided which show the expression profile of the same set of miRNAs in the same type of biological sample, in particular in a blood and/or serum sample, obtained from one or more healthy subjects. A comparison to said reference expression profile(s) allows for the diagnosis of the disease.

Another preferred embodiment of the present invention is a kit for diagnosing diagnosis/differentiation of COPD from lung cancer, comprising means for determining the expression profile of one or more miRNAs selected from the group consisting of hsa-miR-369-5p, hsa-miR-662, hsa-miR-636, hsa-miR-26a, hsa-miR-383, hsa-miR-92a, hsa-miR-328, hsa-miR-675, hsa-miR-1224-3p, hsa-let-7d*, hsa-miR-641, hsa-miR-25, hsa-miR-93*, hsa-miR-940, hsa-miR-631, hsa-miR-513b, hsa-miR-130b, hsa-miR-30e*, hsa-miR-193a-3p, hsa-miR-323-3p, hsa-miR-634, hsa-miR-224, hsa-miR-200a*, hsa-miR-205, hsa-miR-363, hsa-miR-877, hsa-miR-1229, hsa-miR-1308, hsa-miR-1911, hsa-miR-376a*, hsa-miR-183, hsa-miR-199a-3p, hsa-miR-1291, hsa-miR-184, hsa-miR-139-5p, hsa-miR-550, hsa-miR-657, hsa-miR-1258, hsa-miR-874, hsa-miR-626, hsa-miR-483-3p, hsa-miR-1260, hsa-miR-1827, hsa-miR-95, hsa-miR-487a, hsa-miR-1271, hsa-miR-126, hsa-miR-1233, hsa-miR-559, hsa-miR-515-3p.

In a preferred embodiment the kit comprises means for determining at least 7, preferably at least 10, 15, 20, 25, 30, 35, 40, 50 ,75, 100 or all of the indicated miRNAs. The kit for diagnosis/differentiation of COPD from lung cancer is particularly suitable for diagnosis/differentiation of COPD from lung cancer in a blood and/or serum sample or in a sample comprising erythrocytes, leukocytes and/or thrombocytes.

The means for determining a predetermined set of miRNAs may for example comprise a microarray comprising miRNA-specific oligonucleotide probes. In a preferred embodiment, the microarray comprises miRNA-specific oligonucleotide probes for the detection of miRNAs. Depending on the intended use of the microarray in the diagnosis or prognosis of a particular disease, probes for detecting different miRNAs may be included.

A microarray intended for use in the diagnosis/differentiation of COPD from lung cancer preferably comprises miRNA specific oligonucleotide probes for one or more miRNAs selected from the group consisting of hsa-miR-369-5p, hsa-miR-662, hsa-miR-636, hsa-miR-26a, hsa-miR-383, hsa-miR-92a, hsa-miR-328, hsa-miR-675, hsa-miR-1224-3p, hsa-let-7d*, hsa-miR-641, hsa-miR-25, hsa-miR-93*, hsa-miR-940, hsa-miR-631, hsa-miR-513b, hsa-miR-130b, hsa-miR-30e*, hsa-miR-193a-3p, hsa-miR-323-3p, hsa-miR-634, hsa-miR-224, hsa-miR-200a*, hsa-miR-205, hsa-miR-363, hsa-miR-877, hsa-miR-1229, hsa-miR-1308, hsa-miR-1911, hsa-miR-376a*, hsa-miR-183, hsa-miR-199a-3p, hsa-miR-1291, hsa-miR-184, hsa-miR-139-5p, hsa-miR-550, hsa-miR-657, hsa-miR-1258, hsa-miR-874, hsa-miR-626, hsa-miR-483-3p, hsa-miR-1260, hsa-miR-1827, hsa-miR-95, hsa-miR-487a, hsa-miR-1271, hsa-miR-126, hsa-miR-1233, hsa-miR-559, hsa-miR-515-3p.

In a preferred embodiment the microarray comprises oligonucleotide probes for determining at least 7, preferably at least 10, 15, 20, 25, 30, 35, 40, 50, 75, 100 or all of the indicated miRNAs.

The microarray can comprise oligonucleotide probes obtained from known or predicted miRNA sequences. The array may contain different oligonucleotide probes for each miRNA, for example one containing the active mature sequence and another being specific for the precursor of the miRNA. The array may also contain controls such as one or more sequences differing from the human orthologs by only a few bases, which can serve as controls for hybridization stringency conditions. It is also possible to include viral miRNAs or putative miRNAs as predicted from bioinformatic tools. Further, it is possible to include appropriate controls for non-specific hybridization on the microarray.

The differential diagnosis is not limited to the example of differentiating COPD from lung cancer. According to the present invention the differential diagnosis is possible for each and every disease as soon as a set of biomarkers are available that exhibit a high diagnostic value to differentiate between the 2 diseases or more general to differentiate between 2 clinical conditions.

In summary the present invention is composed of the following items:

-   1. A method of diagnosing a disease, comprising the steps     -   (a) determining an expression profile of a predetermined set of         non-coding RNAs, including miRNAs, in a biological sample from a         patient; and     -   (b) comparing said expression profile to a reference expression         profile,         wherein the comparison of said determined expression profile to         said reference expression profile allows for the diagnosis of         the disease, wherein the disease is COPD. -   2. The method according to any one of item 1, wherein the expression     profile is determined of non-coding RNAs, including miRNAs selected     from the group consisting of hsa-miR-99b*, hsa-miR-99b,     hsa-miR-99a*, hsa-miR-99a, hsa-miR-98, hsa-miR-96*, hsa-miR-96,     hsa-miR-95, hsa-miR-944, hsa-miR-943, hsa-miR-942, hsa-miR-941,     hsa-miR-940, hsa-miR-939, hsa-miR-938, hsa-miR-937, hsa-miR-936,     hsa-miR-935, hsa-miR-934, hsa-miR-933, hsa-miR-93*, hsa-miR-93,     hsa-miR-92b*, hsa-miR-92b, hsa-miR-92a-2*, hsa-miR-92a-1*,     hsa-miR-92a, hsa-miR-924, hsa-miR-922, hsa-miR-921, hsa-miR-920,     hsa-miR-9*, hsa-miR-9, hsa-miR-892b, hsa-miR-892a, hsa-miR-891 b,     hsa-miR-891 a, hsa-miR-890, hsa-miR-889, hsa-miR-888*, hsa-miR-888,     hsa-miR-887, hsa-miR-886-5p, hsa-miR-886-3p, hsa-miR-885-5p,     hsa-miR-885-3p, hsa-miR-877*, hsa-miR-877, hsa-miR-876-5p,     hsa-miR-876-3p, hsa-miR-875-5p, hsa-miR-875-3p, hsa-miR-874,     hsa-miR-873, hsa-miR-802, hsa-miR-770-5p, hsa-miR-769-5p,     hsa-miR-769-3p, hsa-miR-767-5p, hsa-miR-767-3p, hsa-miR-766,     hsa-miR-765, hsa-miR-764, hsa-miR-762, hsa-miR-761, hsa-miR-760,     hsa-miR-759, hsa-miR-758, hsa-miR-744*, hsa-miR-744, hsa-miR-720,     hsa-miR-7-2*, hsa-miR-718, hsa-miR-711, hsa-miR-7-1*, hsa-miR-708*,     hsa-miR-708, hsa-miR-7, hsa-miR-675*, hsa-miR-675, hsa-miR-671-5p,     hsa-miR-671-3p, hsa-miR-670, hsa-miR-668, hsa-miR-665, hsa-miR-664*,     hsa-miR-664, hsa-miR-663b, hsa-miR-663, hsa-miR-662, hsa-miR-661,     hsa-miR-660, hsa-miR-659, hsa-miR-658, hsa-miR-657, hsa-miR-656,     hsa-miR-655, hsa-miR-654-5p, hsa-miR-654-3p, hsa-miR-653,     hsa-miR-652, hsa-miR-651, hsa-miR-650, hsa-miR-649, hsa-miR-648,     hsa-miR-647, hsa-miR-646, hsa-miR-645, hsa-miR-644, hsa-miR-643,     hsa-miR-642, hsa-miR-641, hsa-miR-640, hsa-miR-639, hsa-miR-638,     hsa-miR-637, hsa-miR-636, hsa-miR-635, hsa-miR-634, hsa-miR-633,     hsa-miR-632, hsa-miR-631, hsa-miR-630, hsa-miR-629*, hsa-miR-629,     hsa-miR-628-5p, hsa-miR-628-3p, hsa-miR-627, hsa-miR-626,     hsa-miR-625*, hsa-miR-625, hsa-miR-624*, hsa-miR-624, hsa-miR-623,     hsa-miR-622, hsa-miR-621, hsa-miR-620, hsa-miR-619, hsa-miR-618,     hsa-miR-617, hsa-miR-616*, hsa-miR-616, hsa-miR-615-5p,     hsa-miR-615-3p, hsa-miR-614, hsa-miR-613, hsa-miR-612, hsa-miR-611,     hsa-miR-610, hsa-miR-609, hsa-miR-608, hsa-miR-607, hsa-miR-606,     hsa-miR-605, hsa-miR-604, hsa-miR-603, hsa-miR-602, hsa-miR-601,     hsa-miR-600, hsa-miR-599, hsa-miR-598, hsa-miR-597, hsa-miR-596,     hsa-miR-595, hsa-miR-593*, hsa-miR-593, hsa-miR-592, hsa-miR-591,     hsa-miR-590-5p, hsa-miR-590-3p, hsa-miR-589*, hsa-miR-589,     hsa-miR-588, hsa-miR-587, hsa-miR-586, hsa-miR-585, hsa-miR-584,     hsa-miR-583, hsa-miR-582-5p, hsa-miR-582-3p, hsa-miR-581,     hsa-miR-580, hsa-miR-579, hsa-miR-578, hsa-miR-577, hsa-miR-576-5p,     hsa-miR-576-3p, hsa-miR-575, hsa-miR-574-5p, hsa-miR-574-3p,     hsa-miR-573, hsa-miR-572, hsa-miR-571, hsa-miR-570, hsa-miR-569,     hsa-miR-568, hsa-miR-567, hsa-miR-566, hsa-miR-564, hsa-miR-563,     hsa-miR-562, hsa-miR-561, hsa-miR-559, hsa-miR-558, hsa-miR-557,     hsa-miR-556-5p, hsa-miR-556-3p, hsa-miR-555, hsa-miR-554,     hsa-miR-553, hsa-miR-552, hsa-miR-551b*, hsa-miR-551b, hsa-miR-551     a, hsa-miR-550*, hsa-miR-550, hsa-miR-549, hsa-miR-548q,     hsa-miR-548p, hsa-miR-548o, hsa-miR-548n, hsa-miR-548m,     hsa-miR-5481, hsa-miR-548k, hsa-miR-548j, hsa-miR-5481,     hsa-miR-548h, hsa-miR-548g, hsa-miR-548f, hsa-miR-548e,     hsa-miR-548d-5p, hsa-miR-548d-3p, hsa-miR-548c-5p, hsa-miR-548c-3p,     hsa-miR-548b-5p, hsa-miR-548b-3p, hsa-miR-548a-5p, hsa-miR-548a-3p,     hsa-miR-545*, hsa-miR-545, hsa-miR-544, hsa-miR-543, hsa-miR-542-5p,     hsa-miR-542-3p, hsa-miR-541*, hsa-miR-541, hsa-miR-539,     hsa-miR-532-5p, hsa-miR-532-3p, hsa-miR-527, hsa-miR-526b*,     hsa-miR-526b, hsa-miR-526a, hsa-miR-525-5p, hsa-miR-525-3p,     hsa-miR-524-5p, hsa-miR-524-3p, hsa-miR-523*, hsa-miR-523,     hsa-miR-522*, hsa-miR-522, hsa-miR-521, hsa-miR-520h, hsa-miR-520g,     hsa-miR-520f, hsa-miR-520e, hsa-miR-520d-5p, hsa-miR-520d-3p,     hsa-miR-520c-5p, hsa-miR-520c-3p, hsa-miR-520b, hsa-miR-520a-5p,     hsa-miR-520a-3p, hsa-miR-519e*, hsa-miR-519e, hsa-miR-519d,     hsa-miR-519c-5p, hsa-miR-519c-3p, hsa-miR-519b-5p, hsa-miR-519b-3p,     hsa-miR-519a*, hsa-miR-519a, hsa-miR-518r, hsa-miR-518f,     hsa-miR-518e*, hsa-miR-518e, hsa-miR-518d-5p, hsa-miR-518d-3p,     hsa-miR-518c*, hsa-miR-518c, hsa-miR-518b, hsa-miR-518a-5p,     hsa-miR-518a-3p, hsa-miR-517c, hsa-miR-517b, hsa-miR-517a,     hsa-miR-517*, hsa-miR-516b*, hsa-miR-516b, hsa-miR-516a-5p,     hsa-miR-516a-3p, hsa-miR-515-5p, hsa-miR-515-3p, hsa-miR-514,     hsa-miR-513c, hsa-miR-513b, hsa-miR-513a-5p, hsa-miR-513a-3p,     hsa-rniR-512-5p, hsa-miR-512-3p, hsa-miR-511, hsa-miR-510,     hsa-miR-509-5p, hsa-miR-509-3p, hsa-miR-509-3-5p, hsa-miR-508-5p,     hsa-miR-508-3p, hsa-miR-507, hsa-miR-506, hsa-miR-505*, hsa-miR-505,     hsa-miR-504, hsa-miR-503, hsa-miR-502-5p, hsa-miR-502-3p,     hsa-miR-501-5p, hsa-miR-501-3p, hsa-miR-500*, hsa-miR-500,     hsa-miR-499-5p, hsa-miR-499-3p, hsa-miR-498, hsa-miR-497*,     hsa-miR-497, hsa-miR-496, hsa-miR-495, hsa-miR-494, hsa-miR-493*,     hsa-miR-493, hsa-miR-492, hsa-miR-491-5p, hsa-miR-491-3p,     hsa-miR-490-5p, hsa-miR-490-3p, hsa-miR-489, hsa-miR-488*,     hsa-miR-488, hsa-miR-487b, hsa-miR-487a, hsa-miR-486-5p,     hsa-miR-486-3p, hsa-miR-485-5p, hsa-miR-485-3p, hsa-miR-484,     hsa-miR-483-5p, hsa-miR-483-3p, hsa-miR-455-5p, hsa-miR-455-3p,     hsa-miR-454*, hsa-miR-454, hsa-miR-453, hsa-miR-452*, hsa-miR-452,     hsa-miR-451, hsa-miR-450b-5p, hsa-miR-450b-3p, hsa-miR-450a,     hsa-miR-449c*, hsa-miR-449c, hsa-miR-449b*, hsa-miR-449b,     hsa-miR-449a, hsa-miR-448, hsa-miR-433, hsa-miR-432*, hsa-miR-432,     hsa-miR-431*, hsa-miR-431, hsa-miR-429, hsa-miR-425*, hsa-miR-425,     hsa-miR-424*, hsa-miR-424, hsa-miR-423-5p, hsa-miR-423-3p,     hsa-miR-422a, hsa-miR-421, hsa-miR-412, hsa-miR-411*, hsa-miR-411,     hsa-miR-410, hsa-miR-409-5p, hsa-miR-409-3p, hsa-miR-384,     hsa-miR-383, hsa-miR-382, hsa-miR-381, hsa-miR-380*, hsa-miR-380,     hsa-miR-379*, hsa-miR-379, hsa-miR-378*, hsa-miR-378, hsa-miR-377*,     hsa-miR-377, hsa-miR-376c, hsa-miR-376b, hsa-miR-376a*,     hsa-miR-376a, hsa-miR-375, hsa-miR-374b*, hsa-miR-374b,     hsa-miR-374a*, hsa-miR-374a, hsa-miR-373*, hsa-miR-373, hsa-miR-372,     hsa-miR-371-5p, hsa-miR-371-3p, hsa-miR-370, hsa-miR-369-5p,     hsa-miR-369-3p, hsa-miR-367*, hsa-miR-367, hsa-miR-365*,     hsa-miR-365, hsa-miR-363*, hsa-miR-363, hsa-miR-362-5p,     hsa-miR-362-3p, hsa-miR-361-5p, hsa-miR-361-3p, hsa-miR-34c-5p,     hsa-miR-34c-3p, hsa-miR-34b*, hsa-miR-34b, hsa-miR-34a*,     hsa-miR-34a, hsa-miR-346, hsa-miR-345, hsa-miR-342-5p,     hsa-miR-342-3p, hsa-miR-340*, hsa-miR-340, hsa-miR-33b*,     hsa-miR-33b, hsa-miR-33a*, hsa-miR-33a, hsa-miR-339-5p,     hsa-miR-339-3p, hsa-miR-338-5p, hsa-miR-338-3p, hsa-miR-337-5p,     hsa-miR-337-3p, hsa-miR-335*, hsa-miR-335, hsa-miR-331-5p,     hsa-miR-331-3p, hsa-miR-330-5p, hsa-miR-330-3p, hsa-miR-329,     hsa-miR-328, hsa-miR-326, hsa-miR-325, hsa-miR-324-5p,     hsa-miR-324-3p, hsa-miR-323-5p, hsa-miR-323-3p, hsa-miR-320d,     hsa-miR-320c, hsa-miR-320b, hsa-miR-320a, hsa-miR-32*, hsa-miR-32,     hsa-miR-31*, hsa-miR-31, hsa-miR-30e*, hsa-miR-30e, hsa-miR-30d*,     hsa-miR-30d, hsa-miR-30c-2*, hsa-miR-30c-1*, hsa-miR-30c,     hsa-miR-30b*, hsa-miR-30b, hsa-miR-30a*, hsa-miR-30a, hsa-miR-302f,     hsa-miR-302e, hsa-miR-302d*, hsa-miR-302d, hsa-miR-302c*,     hsa-miR-302c, hsa-miR-302b*, hsa-miR-302b, hsa-miR-302a*,     hsa-miR-302a, hsa-miR-301 b, hsa-miR-301 a, hsa-miR-300,     hsa-miR-29c*, hsa-miR-29c, hsa-miR-29b-2*, hsa-miR-29b-1*,     hsa-miR-29b, hsa-miR-29a*, hsa-miR-29a, hsa-miR-299-5p,     hsa-miR-299-3p, hsa-miR-298, hsa-miR-297, hsa-miR-296-5p,     hsa-miR-296-3p, hsa-miR-28-5p, hsa-miR-28-3p, hsa-miR-27b*,     hsa-miR-27b, hsa-miR-27a*, hsa-miR-27a, hsa-miR-26b*, hsa-miR-26b,     hsa-miR-26a-2*, hsa-miR-26a-1*, hsa-miR-26a, hsa-miR-25*,     hsa-miR-25, hsa-miR-24-2*, hsa-miR-24-1*, hsa-miR-24, hsa-miR-23b*,     hsa-miR-23b, hsa-miR-23a*, hsa-miR-23a, hsa-miR-2278, hsa-miR-2277,     hsa-miR-2276, hsa-miR-224*, hsa-miR-224, hsa-miR-223*, hsa-miR-223,     hsa-miR-222*, hsa-miR-222, hsa-miR-221*, hsa-miR-221, hsa-miR-220c,     hsa-miR-220b, hsa-miR-220a, hsa-miR-22*, hsa-miR-22, hsa-miR-219-5p,     hsa-miR-219-2-3p, hsa-miR-219-1-3p, hsa-miR-218-2*, hsa-miR-218-1*,     hsa-miR-218, hsa-miR-217, hsa-miR-216b, hsa-miR-216a, hsa-miR-215,     hsa-miR-214*, hsa-miR-214, hsa-miR-212, hsa-miR-2117, hsa-miR-2116*,     hsa-miR-2116, hsa-miR-2115*, hsa-miR-2115, hsa-miR-2114*,     hsa-miR-2114, hsa-miR-2113, hsa-miR-2110, hsa-miR-211, hsa-miR-210,     hsa-miR-21*, hsa-miR-21, hsa-miR-20b*, hsa-miR-20b, hsa-miR-20a*,     hsa-miR-20a, hsa-miR-208b, hsa-miR-208a, hsa-miR-206, hsa-miR-2054,     hsa-miR-2053, hsa-miR-2052, hsa-miR-205*, hsa-miR-205, hsa-miR-204,     hsa-miR-203, hsa-miR-202*, hsa-miR-202, hsa-miR-200c*, hsa-miR-200c,     hsa-miR-200b*, hsa-miR-200b, hsa-miR-200a*, hsa-miR-200a,     hsa-miR-19b-2*, hsa-miR-19b-1*, hsa-miR-19b, hsa-miR-19a*,     hsa-miR-19a, hsa-miR-199b-5p, hsa-miR-199b-3p, hsa-miR-199a-5p,     hsa-miR-199a-3p, hsa-miR-198, hsa-miR-1979, hsa-miR-1978,     hsa-miR-1977, hsa-miR-1976, hsa-miR-1975, hsa-miR-1974,     hsa-miR-1973, hsa-miR-1972, hsa-miR-197, hsa-miR-196b*,     hsa-miR-196b, hsa-miR-196a*, hsa-miR-196a, hsa-rniR-195*,     hsa-miR-195, hsa-miR-194*, hsa-miR-194, hsa-miR-193b*, hsa-miR-193b,     hsa-miR-193a-5p, hsa-miR-193a-3p, hsa-miR-192*, hsa-miR-192,     hsa-miR-1915*, hsa-miR-1915, hsa-miR-1914*, hsa-miR-1914,     hsa-miR-1913, hsa-miR-1912, hsa-miR-1911*, hsa-miR-1911,     hsa-miR-1910, hsa-miR-191*, hsa-miR-191, hsa-miR-190b,     hsa-miR-1909*, hsa-miR-1909, hsa-miR-1908, hsa-miR-190,     hsa-miR-18b*, hsa-miR-18b, hsa-miR-18a*, hsa-miR-18a,     hsa-miR-188-5p, hsa-miR-188-3p, hsa-miR-187*, hsa-miR-187,     hsa-miR-186*, hsa-miR-186, hsa-miR-185*, hsa-miR-185, hsa-miR-184,     hsa-miR-183*, hsa-miR-183, hsa-miR-1827, hsa-miR-1826, hsa-miR-1825,     hsa-miR-182*, hsa-miR-182, hsa-miR-181d, hsa-miR-181c*,     hsa-miR-181c, hsa-miR-181b, hsa-miR-181a-2*, hsa-miR-181a*,     hsa-miR-181a, hsa-miR-17*, hsa-miR-17, hsa-miR-16-2*, hsa-miR-16-1*,     hsa-miR-16, hsa-miR-15b*, hsa-miR-15b, hsa-miR-15a*, hsa-miR-15a,     hsa-miR-155*, hsa-miR-155, hsa-miR-154*, hsa-miR-154, hsa-miR-1539,     hsa-miR-1538, hsa-miR-1537, hsa-miR-153, hsa-miR-152,     hsa-miR-151-5p, hsa-miR-151-3p, hsa-miR-150*, hsa-miR-150,     hsa-miR-149*, hsa-miR-149, hsa-miR-148b*, hsa-miR-148b,     hsa-miR-148a*, hsa-miR-148a, hsa-miR-147b, hsa-miR-1471,     hsa-miR-1470, hsa-miR-147, hsa-miR-146b-5p, hsa-miR-146b-3p,     hsa-miR-146a*, hsa-miR-146a, hsa-miR-1469, hsa-miR-1468,     hsa-miR-145*, hsa-miR-145, hsa-miR-144*, hsa-miR-144, hsa-miR-143*,     hsa-miR-143, hsa-miR-142-5p, hsa-miR-142-3p, hsa-miR-141*,     hsa-miR-141, hsa-miR-140-5p, hsa-miR-140-3p, hsa-miR-139-5p,     hsa-miR-139-3p, hsa-miR-138-2*, hsa-miR-138-1*, hsa-miR-138,     hsa-miR-137, hsa-miR-136*, hsa-miR-136, hsa-miR-135b*, hsa-miR-135b,     hsa-miR-135a*, hsa-miR-135a, hsa-miR-134, hsa-miR-133b,     hsa-miR-133a, hsa-miR-1324, hsa-miR-1323, hsa-miR-1322,     hsa-miR-1321, hsa-miR-132*, hsa-miR-132, hsa-miR-130b*,     hsa-miR-130b, hsa-miR-130a*, hsa-miR-130a, hsa-miR-1308,     hsa-miR-1307, hsa-miR-1306, hsa-miR-1305, hsa-miR-1304,     hsa-miR-1303, hsa-miR-1302, hsa-miR-1301, hsa-miR-1299,     hsa-miR-1298, hsa-miR-1297, hsa-miR-1296, hsa-miR-129-5p,     hsa-miR-1295, hsa-miR-1294, hsa-miR-129-3p, hsa-miR-1293,     hsa-miR-1292, hsa-miR-1291, hsa-miR-1290, hsa-miR-129*,     hsa-miR-1289, hsa-miR-1288, hsa-miR-1287, hsa-miR-1286,     hsa-miR-1285, hsa-miR-1284, hsa-miR-1283, hsa-miR-1282,     hsa-miR-1281, hsa-miR-1280, hsa-miR-128, hsa-miR-1279, hsa-miR-1278,     hsa-miR-1277, hsa-miR-1276, hsa-miR-127-5p, hsa-miR-1275,     hsa-miR-1274b, hsa-miR-1274a, hsa-miR-127-3p, hsa-miR-1273,     hsa-miR-1272, hsa-miR-1271, hsa-miR-1270, hsa-miR-1269,     hsa-miR-1268, hsa-miR-1267, hsa-miR-1266, hsa-miR-1265,     hsa-miR-1264, hsa-miR-1263, hsa-miR-1262, hsa-miR-1261,     hsa-miR-1260, hsa-miR-126*, hsa-miR-126, hsa-miR-125b-2*,     hsa-miR-125b-1*, hsa-miR-125b, hsa-miR-125a-5p, hsa-miR-125a-3p,     hsa-miR-1259, hsa-miR-1258, hsa-miR-1257, hsa-miR-1256,     hsa-miR-1255b, hsa-miR-1255a, hsa-miR-1254, hsa-miR-1253,     hsa-miR-1252, hsa-miR-1251, hsa-miR-1250, hsa-miR-1249,     hsa-miR-1248, hsa-miR-1247, hsa-miR-1246, hsa-miR-1245,     hsa-miR-1244, hsa-miR-1243, hsa-miR-124*, hsa-miR-124, hsa-miR-1238,     hsa-miR-1237, hsa-miR-1236, hsa-miR-1234, hsa-miR-1233,     hsa-miR-1231, hsa-miR-1229, hsa-miR-1228*, hsa-miR-1228,     hsa-miR-1227, hsa-miR-1226*, hsa-miR-1226, hsa-miR-1225-5p,     hsa-miR-1225-3p, hsa-miR-1224-5p, hsa-miR-1224-3p, hsa-miR-122*,     hsa-miR-122, hsa-miR-1208, hsa-miR-1207-5p, hsa-miR-1207-3p,     hsa-miR-1206, hsa-miR-1205, hsa-miR-1204, hsa-miR-1203,     hsa-miR-1202, hsa-miR-1201, hsa-miR-1200, hsa-miR-1197,     hsa-miR-1185, hsa-miR-1184, hsa-miR-1183, hsa-miR-1182,     hsa-miR-1181, hsa-miR-1180, hsa-miR-1179, hsa-miR-1178,     hsa-miR-10b*, hsa-miR-10b, hsa-miR-10a*, hsa-miR-10a, hsa-miR-107,     hsa-miR-106b*, hsa-miR-106b, hsa-miR-106a*, hsa-miR-106a,     hsa-miR-105*, hsa-miR-105, hsa-miR-103-as, hsa-miR-103-2*,     hsa-miR-103, hsa-miR-101*, hsa-miR-101, hsa-miR-100*, hsa-miR-100,     hsa-miR-1, hsa-let-7i*, hsa-let-7i, hsa-let-7g*, hsa-let-7g,     hsa-let-7f-2*, hsa-let-7f-1*, hsa-let-7f, hsa-let-7e*, hsa-let-7e,     hsa-let-7d*, hsa-let-7d, hsa-let-7c*, hsa-let-7c, hsa-let-7b*,     hsa-let-7b, hsa-let-7a-2*, hsa-let-7a*, hsa-let-7a, hsa-life-1,     hsa-life-2, hsa-life-2-AS, hsa-life-3, hsa-life-4, hsa-life-6-5p,     hsa-life-6-3p, hsa-life-7-AS, hsa-life-7, hsa-life-9, hsa-life-9-AS,     hsa-life-11, hsa-life-12-5p, hsa-life-12-3p, hsa-life-13-3p,     hsa-life-13-5p, hsa-life-14-3p, hsa-life-14-5p, hsa-life-17,     hsa-life-21, hsa-life-22, hsa-life-26-3p, hsa-life-26-5p,     hsa-life-27, hsa-life-31-5p, hsa-life-31-3p, hsa-life-33-AS,     hsa-life-33, hsa-life-36-3p, hsa-life-36-5p, hsa-life-37-3p,     hsa-life-37-5p, hsa-life-5-5p, hsa-life-5-3p, hsa-life-8,     hsa-life-10, hsa-life-15-3p, hsa-life-15-5p, hsa-life-16-5p,     hsa-life-16-3p, hsa-life-18, hsa-life-19-5p, hsa-life-19-3p,     hsa-life-20-3p, hsa-life-20-5p, hsa-life-23-3p, hsa-life-23-5p,     hsa-life-24, hsa-life-25, hsa-life-28-3p, hsa-life-28-5p,     hsa-life-29, hsa-life-30, hsa-life-32-AS, hsa-life-32,     hsa-life-34-3p, hsa-life-34-5p, hsa-life-35. -   3. The method according to item 1 or 2, wherein the predetermined     set of miRNAs representative for diagnosis of COPD comprises at     least 1, 7, 10, 15, 20, 25, 30, 35, 40, 50, 75, 100 non-coding RNAs     including miRNAs. -   4. The method according to any one of items 1-3, wherein the     predetermined set of miRNAs representative for diagnosis of COPD     comprises at least 1, 7, 10, 15, 20, 25, 30, 35, 40, 50, 75, 100 of     the miRNAs selected from the group consisting of hsa-miR-182*,     hsa-miR-205, hsa-miR-662, hsa-miR-636, hsa-miR-554, hsa-miR-1233,     hsa-miR-330-3p, hsa-miR-492, hsa-miR-383, hsa-miR-518b, hsa-miR-23b,     hsa-miR-188-5p, hsa-miR-216a, hsa-miR-625*, hsa-miR-26a,     hsa-miR-30c, hsa-miR-200a*, hsa-miR-24-1*, hsa-miR-641, hsa-miR-32,     hsa-miR-646, hsa-miR-92a, hsa-miR-566, hsa-miR-1291, hsa-miR-23a,     hsa-miR-24-2*, hsa-miR-548p, hsa-miR-1470, hsa-miR-199a-5p,     hsa-miR-25, hsa-miR-136, hsa-miR-342-5p, hsa-miR-1229,     hsa-miR-369-5p, hsa-miR-634, hsa-miR-1913, hsa-miR-548o,     hsa-miR-183, hsa-miR-182, hsa-miR-129-3p, hsa-miR-500*,     hsa-miR-1308, hsa-miR-1471, hsa-miR-195, hsa-miR-361-5p,     hsa-miR-515-5p, hsa-miR-224, hsa-miR-151-5p, hsa-miR-564,     hsa-miR-934, hsa-miR-146b-5p, hsa-miR-214, hsa-miR-216b,     hsa-miR-28-5p, hsa-miR-940, hsa-miR-606, hsa-miR-631, hsa-miR-21*,     hsa-miR-384, hsa-miR-1234, hsa-miR-1260, hsa-miR-532-3p,     hsa-miR-122*, hsa-miR-199a-3p, hsa-miR-33b*, hsa-miR-184,     hsa-miR-373*, hsa-miR-145, hsa-miR-27a, hsa-miR-1258, hsa-miR-124,     hsa-miR-489, hsa-miR-559, hsa-miR-223. -   5. The method according to any one of items 1-4 wherein said     biological sample is selected from blood and/or serum or urine     samples. -   6. The method according to any one of items 1-5 wherein miRNA the     expression profile is determined by nucleic acid hybridization,     nucleic acid amplification, polymerase extension, sequencing, mass     spectroscopy or any combinations thereof. -   7. The method according to any one of items 1-6, wherein the miRNA     expression profile of said subject and the reference expression     profiles and optionally the predetermined set of miRNAs are stored     in a database. -   8. The method according to any one of items 1-7, wherein the     biological sample is not labeled prior to determination of the     expression profile. -   9. The method according to any one of items 1-8 wherein the     diagnosis comprises determining survival rate, responsiveness to     drugs, and/or monitoring the course of the disease or the therapy,     e.g. chemotherapy. -   10. The method of item 9 wherein the nucleic acid hybridisation is     performed using a solid-phase nucleic acid biochip array, in     particular a microarray or in situ hybridisation, and/or wherein the     nucleic acid amplification is performed via a real-time PCR     (RT-PCR). -   11. A kit for diagnosing and/or predicting COPD of a subject,     comprising:     -   (a) means for determining the miRNA expression profile of a RNA         sample of a subject, and     -   (b) at least one reference set of miRNA profile characteristic         for a particular condition.

So far, miRNAs have been extensively studied in tissue material. It has been found that miRNAs are expressed in a highly tissue-specific manner. Disease-specific expression of miRNAs have been reported in many human cancers employing primarily tissue material as the miRNA source. In this context miRNAs expression profiles were found to be useful in identifying the tissue of origin for cancers of unknown primary origin. Since recently it is known that miRNAs are not only present in tissues but also in other body fluid samples, including human blood. Nevertheless, the mechanism why miRNAs are found in body fluids, especially in blood, or their function in these body fluids is not understood yet.

Various miRNA biomarkers found in tissue material have been proposed to be correlated with certain diseases, e.g. cancer. However, there is still a need for novel miRNAs as biomarkers for the detection and/or prediction of these and other types of diseases. Especially desirable are non-invasive biomarkers, that allow for quick, easy and cost-effective diagnosis/prognosis which cause only minimal stress for the patient eliminating the need for surgical intervention

Particularly, the potential role of miRNAs as non-invasive biomarkers for the diagnosis and/or prognosis of COPD has not been systematically evaluated yet. In addition, many of the miRNA biomarkers presently available for diagnosing and/or prognosing of diseases have shortcomings such as reduced sensitivity, not sufficient specificity or do not allow timely diagnosis or represent invasive biomarkers. Accordingly, there is still a need for novel and efficient miRNAs or sets of miRNAs as markers, effective methods and kits for the non-invasive diagnosis and/or prognosis of diseases such as COPD.

The inventors of the present invention assessed for the first time the expression of miRNAs on a whole-genome level in subjects with COPD as non-invasive biomarkers from body fluids, preferably in blood. They surprisingly found that miRNAs are significantly dysregulated in blood of COPD subjects in comparison to healthy controls or lung cancer patients and thus, miRNAs are appropriated non-invasive biomarkers for diagnosing and/or prognosing of COPD. This finding is surprising, since there is nearly no overlap of the miRNA biomarkers found in blood and the miRNA biomarkers found in tissue material representing the origin of the disease. The inventors of the present invention surprisingly found miRNA biomarkers in body fluids, especially in blood, that have not been found to be correlated to COPD when tissues material was used for this kind of analysis. Therefore, the inventors of the invention identified for the first time miRNAs as non-invasive surrogate biomarkers for diagnosis and/or prognosis of COPD. The inventors of the present invention identified single miRNAs which predict COPD with high specificity, sensitivity and accuracy. The inventors of the present invention also pursued a multiple biomarker strategy, thus implementing sets of miRNA biomarkers for diagnosing and/or prognosing of COPD leading to added specificity, sensitivity, accuracy and predictive power, thereby circumventing the limitations of single biomarker. In detail, by using a machine learning algorithms, they identified unique sets of miRNAs (miRNA signatures) that allow for non-invasive diagnosis of COPD with even higher power, indicating that sets of miRNAs (miRNA signatures) derived from a body fluid sample, such as blood from a subject (e.g. human) can be used as novel non-invasive biomarkers.

The inventors of the present invention surprisingly found that miRNAs are significantly dysregulated in body fluid samples such as blood of COPD subjects in comparison to a cohort of controls (healthy subjects or lung cancer patients) and thus, miRNAs are appropriated biomarkers for diagnosing and/or prognosing of COPD in a non-invasive fashion. Furthermore, the predetermined sets of miRNAs of the present invention lead to high performance in diagnosing and/or prognosing of COPD, thus expose very high specificity, sensitivity and accuracy. They succeeded in determining the miRNAs that are differentially regulated in body fluid samples from patients having COPD compared to a cohort of controls (healthy subjects or lung cancer patients) (see experimental section for experimental details). Additionally, the inventors of the present invention performed hypothesis tests (e.g. t-test, limma-test) or other measurements (e.g. AUC, mutual information) on the expression level of the found miRNAs, in all controls (healthy subjects) and subjects suffering from COPD. These tests resulted in a significance value (p-value) for each miRNA. This p-value is a measure for the diagnostic power of each of these single miRNAs to discriminate, for example, between the two clinical conditions: controls (healthy subjects or lung cancer patients), i.e. not suffering from COPD, or diseased, i.e. suffering from COPD. Since a manifold of tests are carried out, one for each miRNA, the p-values may be too optimistic and, thus, over-estimate the actual discriminatory power. Hence, the p-values are corrected for multiple testing by the Benjamini Hochberg approach.

The term “body fluid sample”, as used in the context of the present invention, refers to liquids originating from the body of a subject. Said body fluid samples include, but are not limited to, blood, urine, sputum, breast milk, cerebrospinal fluid, cerumen (earwax), endolymph, perilymph, gastric juice, mucus, peritoneal fluid, pleural fluid, saliva, sebum (skin oil), semen, sweat, tears, vaginal secretion, vomit including components or fractions thereof. Said body fluid samples may be mixed or pooled, e.g. a body fluid sample may be a mixture of blood and urine samples or blood and tissue material. A “body fluid sample” may be provided by removing a body liquid from a subject, but may also be provided by using previously isolated sample material. Preferably, the body fluid sample from a subject (e.g. human or animal) has a volume of between 0.1 and 20 ml, more preferably of between 0.5 and 10 ml, more preferably between 1 and 8 ml and most preferably between 2 and 5 ml, i.e. 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 2, 2.5, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 ml. In the context of the present invention said “body fluid sample” allows for a non-invasive diagnosis/and or prognosis of a subject.

The term “blood sample”, as used in the context of the present invention, refers to a blood sample originating from a subject. The “blood sample” may be derived by removing blood from a subject by conventional blood collecting techniques, but may also be provided by using previously isolated and/or stored blood samples. For example a blood sample may be whole blood, plasma, serum, PBMC (peripheral blood mononuclear cells), blood cellular fractions including red blood cells (erythrocytes), white blood cells (leukocytes), platelets (thrombocytes), or blood collected in blood collection tubes (e.g. EDTA-, heparin-, citrate-, PAXgene- , Tempus-tubes) including components or fractions thereof. For example, a blood sample may be taken from a subject suspected to be affected or to be suspected to be affected by COPD, prior to initiation of a therapeutic treatment, during the therapeutic treatment and/or after the therapeutic treatment. Preferably, the blood sample from a subject (e.g. human or animal) has a volume of between 0.1 and 20 ml, more preferably of between 0.5 and 10 ml, more preferably between 1 and 8 ml and most preferably between 2 and 5 ml, i.e. 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 2, 2.5, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 ml. In the context of the present invention said “body fluid sample” allows for a non-invasive diagnosis/and or prognosis of a subject.

Preferably, when the blood sample is collected from the subject the RNA-fraction, especially the the miRNA fraction, is guarded against degradation. For this purpose special collection tubes (e.g. PAXgene RNA tubes from Preanalytix, Tempus Blood RNA tubes from Applied Biosystems) or additives (e.g. RNAlater from Ambion, RNAsin from Promega) that stabilize the RNA fraction and/or the miRNA fraction are employed.

The biological sample, preferably the body fluid sample may be from a subject (e.g. human or mammal) that has been therapeutically treated or that has not been therapeutically treated. In one embodiment, the therapeutical treatment is monitored on the basis of the detection of the miRNA or set of miRNAs by the polynucleotide or set of polynucleotides of the invention. It is also preferred that total RNA or a subfraction thereof, isolated (e.g. extracted) from a biological sample of a subject (e.g. human or animal), is used for detecting the miRNA or set of miRNAs by the polynucleotide or set of polynucleotides or primer pairs of the invention.

The term “non-invasive”, as used in the context of the present invention, refers to methods for obtaining a biological sample, particularly a body fluid sample, without the need for an invasive surgical intervention or invasive medical procedure. In the context of the present invention, a blood drawn represents a non-invasive procedure, therefore a blood-based test (utilizing blood or fractions thereof) is a non-invasive test. Other body fluid samples for non-invasive tests are e.g. urine, sputum, tears, mothers mild, cerumen, sweat, saliva, vaginal secretion, vomit, etc.

The term “diagnosis” as used in the context of the present invention refers to the process of determining a possible disease or disorder and therefore is a process attempting to define the (clinical) condition of a subject. The determination of the expression level of a set of miRNAs according to the present invention correlates with the (clinical) condition of a subject. Preferably, the diagnosis comprises (i) determining the occurrence/presence of COPD, (ii) monitoring the course of COPD, (iii) staging of COPD, (iv) measuring the response of a patient with COPD to therapeutic intervention, and/or (v) segmentation of a subject suffering from COPD.

The term “prognosis” as used in the context of the present invention refers to describing the likelihood of the outcome or course of a disease or a disorder. Preferably, the prognosis comprises (i) identifying of a subject who has a risk to develop COPD, (ii) predicting/estimating the occurrence, preferably the severity of occurrence of COPD, and/or(iii) predicting the response of a subject with COPD to therapeutic intervention.

The term “suffering or suspected to be suffering from COPD” as used in the context of the present invention comprises the diagnosis and/or prognosis of COPD in a suspect as defined above.

In a first aspect, the present invention relates to a method for diagnosing and/or prognosing of COPD comprising the steps of:

-   -   (i) determining an expression profile of a predetermined set         comprising at least two miRNAs representative for COPD in a body         fluid sample from a subject, and     -   (ii) comparing said expression profile to a reference expression         profile, wherein the comparison of said expression profile to         said reference expression profile allows for the diagnosis         and/or prognosis of COPD,

It is preferred that the body fluid sample is a blood sample, particularly preferred it is a whole blood, PBMC, serum or plasma sample, more particularly preferred it is a whole blood sample.

It is preferred that the subject is a mammal including both a human and another mammal, e.g. an animal such as a mouse, a rat, a rabbit, or a monkey. It is particularly preferred that the subject is a human.

In one embodiment of the first aspect of the present invention, the predetermined set of miRNAs comprises miRNAs that are differentially regulated in blood samples from COPD patients as compared to healthy controls.

Preferably, the predetermined set comprising at least two miRNAs that are differentially regulated in blood samples from COPD patients as compared to healthy controls is selected from the set of miRNAs listed in FIG. 3 or FIG. 7.

It is preferred that the predetermined set comprising at least two miRNAs that are differentially regulated in blood samples from COPD patients as compared to healthy controls is selected from the sets of miRNAs listed in FIG. 8 (SNC-1 to SNC-801).

It is also preferred that the predetermined set comprising at least two miRNAs that are differentially regulated in blood samples from COPD patients as compared to healthy controls comprises at least one set of miRNAs listed in FIG. 8.

Further, according to the method of the present invention, for determining an expression profile of the predetermined set comprising at least two miRNAs that are differentially regulated in blood samples from COPD patients as compared to healthy controls in a body fluid sample from a subject comprises the miRNAs from one set or a plurality of sets of miRNAs listed in FIG. 8.

For example, a set comprising 30 miRNAs that are differentially regulated in blood samples from COPD patients as compared to healthy controls in a body fluid sample from a subject comprises at least the miRNAs from one predetermined set or several sets of miRNAs listed in FIG. 8. Alternatively, a set comprising 29, 28, 27,26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4 or 3 miRNAs that are differentially regulated in blood samples from COPD patients as compared to healthy controls comprises at least the miRNAs from one set or several sets of miRNAs listed in FIG. 8.

Further, according to the method of the present invention, for determining an expression profile of the predetermined set comprising at least two that are differentially regulated in blood samples from COPD patients as compared to healthy controls in a body fluid sample from a subject comprises combinations of sets of miRNAs listed in FIG. 8.

For example, said predetermined set comprising 30 miRNAs that are differentially regulated in blood samples from COPD patients as compared to healthy controls in a body fluid sample from a subject comprises at least 2, e.g. 2, 3, 4, 5 or 6, sets of miRNAs listed in FIG. 8. Alternatively, said set comprising 29, 28, 27 ,26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5 or 4 miRNAs comprises a least 2, e.g. 2, 3, 4, 5 or 6, sets of miRNAs listed in FIG. 8.

The reference expression profile may be obtained from at least two subjects (e.g. human or animal). Preferably, the reference expression profile is an average expression profile (data) of at least 2 to 400 subjects, more preferably at least 20 to 200 subjects, and most preferably at least 40 to 150 subjects, with two known clinical conditions which are COPD or a specific form of COPD and healthy control.

In second embodiment of the first aspect of the present invention, the predetermined set of miRNAs comprises miRNAs that are differentially regulated in blood samples from COPD patients as compared to lung cancer patients.

Preferably, the predetermined set comprising at least two miRNAs that are differentially regulated in blood samples from COPD patients as compared to lung cancer patients is selected from the set of miRNAs listed in FIG. 5 or FIG. 9.

It is preferred that the predetermined set comprising at least two miRNAs that are differentially regulated in blood samples from COPD patients as compared to lung cancer patients is selected from the sets of miRNAs listed in FIG. 10 (SLC-1 to SLC-901).

It is also preferred that the predetermined set comprising at least two miRNAs that are differentially regulated in blood samples from COPD patients as compared to healthy controls comprises at least one set of miRNAs listed in FIG. 10.

Further, according to the method of the present invention, for determining an expression profile of the predetermined set comprising at least two miRNAs that are differentially regulated in blood samples from COPD patients as compared to lung cancer patients in a body fluid sample from a subject comprises the miRNAs from one set or a plurality of sets of miRNAs listed in FIG. 10.

For example, a set comprising 30 miRNAs that are differentially regulated in blood samples from COPD patients as compared to lung cancer patients in a body fluid sample from a subject comprises at least the miRNAs from one predetermined set or several sets of miRNAs listed in FIG. 10.

Alternatively, a set comprising 29, 28, 27,26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4 or 3 miRNAs that are differentially regulated in blood samples from COPD patients as compared to lung cancer patients comprises at least the miRNAs from one set or several sets of miRNAs listed in FIG. 10.

Further, according to the method of the present invention, for determining an expression profile of the predetermined set comprising at least two that are differentially regulated in blood samples from COPD patients as compared to lung cancer patients in a body fluid sample from a subject comprises combinations of sets of miRNAs listed in FIG. 10.

For example, said predetermined set comprising 30 miRNAs that are differentially regulated in blood samples from COPD patients as compared to lung, cancer patients in a body fluid sample from a subject comprises at least 2, e.g. 2, 3, 4, 5 or 6, sets of miRNAs listed in FIG. 10. Alternatively, said set comprising 29, 28, 27 ,26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5 or 4 miRNAs comprises a least 2, e.g. 2, 3, 4, 5 or 6, sets of miRNAs listed in FIG. 10.

The reference expression profile may be obtained from at least two subjects (e.g. human or animal). Preferably the reference expression profile is an average expression profile (data) of at least 2 to 400 subjects, more preferably at least 20 to 200 subjects, and most preferably at least 40 to 150 subjects, with two known clinical conditions which are COPD or a specific form of COPD and lung cancer or a specific form of lung cancer.

It is particularly preferred that the reference expression profile is an algorithm or mathematical function. Preferably the algorithm or mathematical function is obtained from a reference expression profile (data) of at least two subjects, preferably the algorithm or mathematical function is obtained from an average reference expression profile (data) of at least 2 to 400 subjects, more preferably of at least 20 to 200 subjects, and most preferably of at least 40 to 150 subjects.

It is preferred that the algorithm or mathematical function is obtained using a machine learning approach.

Preferably, the algorithm or mathematical function is saved on a data carrier comprised in the kit (according to the seventh aspect of the invention) or the computer program, wherein the algorithm or mathematical function is comprised, is saved on a data carrier comprised in the kit.

It is preferred that the miRNA expression profile may be generated by any convenient means, e.g. nucleic acid hybridization (e.g. to a microarray), nucleic acid amplification (PCR, RT-PCR, qRT-PCR, high-throughput RT-PCR), ELISA for quantitation, next generation sequencing (e.g. ABI SOLID, Illumina Genome Analyzer, Roche/454 GS FLX), flow cytometry (e.g. LUMINEX) and the like, that allow the analysis of differential miRNA expression levels between samples of a subject (e.g. diseased) and a control subject (e.g. healthy, reference sample).

Nucleic acid hybridization may be performed using a microarray/biochip or in situ hybridization. In situ hybridization is preferred for the analysis of a single miRNA or a set comprising a low number of miRNAs (e.g. a set of at least 2 to 50 miRNAs such as a set of 2, 5, 10, 20, 30, or 40 miRNAs). The microarray/biochip, however, allows the analysis of a single miRNA as well as a complex set of miRNAs (e.g. a all known miRNAs or subsets therof).

Nucleic acid amplification may be performed using real time polymerase chain reaction (RT-PCR) such as real time quantitative polymerase chain reaction (RT qPCR). The standard real time polymerase chain reaction (RT-PCR) is preferred for the analysis of a single miRNA or a set comprising a low number of miRNAs (e.g. a set of at least 2 to 50 miRNAs such as a set of 2, 5, 10, 20, 30, or 40 miRNAs), whereas high-throughput RT-PCR technologies (e.g. OpenArray from Applied Biosystems, SmartPCR from

Wafergen, Biomark System from Fluidigm) are also able to measure large sets of miRNAS (e.g a set of 10, 20, 30, 50, 80, 100, 200 or more) or all known miRNAs in a high parallel fashion. RT-PCR is particularly suitable for detecting low abundant miRNAs.

In a second aspect, the invention relates to a set comprising polynucleotides for detecting a predetermined set comprising at least two miRNAs for diagnosing and/or prognosing of COPD in a body fluid sample from a subject.

It is preferred that the body fluid sample is a blood sample, particularly preferred it is a whole blood, PBMC, serum or plasma sample, more particularly preferred it is a whole blood sample.

It is preferred that the subject is a mammal including both a human and another mammal, e.g. an animal such as a mouse, a rat, a rabbit, or a monkey. It is particularly preferred that the subject is a human.

In one embodiment of the second aspect of the present invention, the predetermined set of miRNAs comprises miRNAs that are differentially regulated in blood samples from COPD patients as compared to healthy controls.

Preferably, the predetermined set comprising at least two miRNAs that are differentially regulated in blood samples from COPD patients as compared to healthy controls is selected from the set of miRNAs listed in FIG. 3 or 7.

It is preferred that the predetermined set comprising at least two miRNAs that are differentially regulated in blood samples from COPD patients as compared to healthy controls is selected from the set of miRNAs listed in FIG. 8.

It is preferred that the predetermined set comprising at least two miRNAs that are differentially regulated in blood samples from COPD patients as compared to healthy controls. comprises at least one set of miRNAs listed in FIG. 8.

It is preferred that the polynucleotides comprised in the set of the present invention are complementary to the miRNAs comprised in the predetermined set, wherein the nucleotide sequences of said miRNAs are preferably selected from the group consisting of miRNAs listed in FIG. 3 or 7 or set of miRNAs listed in FIG. 8, a fragment thereof, and a sequence having at least 80%, 85%, 90% or 95% sequence identity thereto.

For example, the polynucleotides of the present invention are for detecting a predetermined set of 40 or 39 or 38 or 37 or 36 or 35 or 34 or 33 or 32 or 31 or 30 or 29 or 28 or 27 or 26 or 25 or 24 or 23 or 22 or 21 or 20 or 19 or 18 or 17 or 16 or 15 or 14 or 13 or 12 or 11 or 10 or 9 or 8 or 7 or 6 or 5 or 4 or 3 miRNAs that are differentially regulated in blood samples from COPD patients as compared to healthy controls wherein the set of miRNAs comprises at least one, e.g. 1, 2, 3, 4, 5 or 6, of the sets of miRNAs listed in FIG. 8.

In a second embodiment of the second aspect of the present invention, the predetermined set of miRNAs comprises miRNAs that are differentially regulated in blood samples from COPD patients as compared to lung cancer patients.

Preferably, the predetermined set comprising at least two miRNAs that are differentially regulated in blood samples from COPD patients as compared to lung cancer patients is selected from the set of miRNAs listed in FIG. 5 or 9.

It is preferred that the predetermined set comprising at least two miRNAs that are differentially regulated in blood samples from COPD patients as compared to lung cancer patients is selected from the set of miRNAs listed in FIG. 10.

It is preferred that the predetermined set comprising at least two miRNAs that are differentially regulated in blood samples from COPD patients as compared to lung cancer patients. comprises at least one set of miRNAs listed in FIG. 10.

It is preferred that the polynucleotides comprised in the set of the present invention are complementary to the miRNAs comprised in the predetermined set, wherein the nucleotide sequences of said miRNAs are preferably selected from the group consisting of miRNAs listed in FIG. 5 or 9 or set of miRNAs listed in FIG. 10, a fragment thereof, and a sequence having at least 80%, 85%, 90% or 95% sequence identity thereto.

For example, the polynucleotides of the present invention are for detecting a predetermined set of 40 or 39 or 38 or 37 or 36 or 35 or 34 or 33 or 32 or 31 or 30 or 29 or 28 or 27 or 26 or 25 or 24 or 23 or 22 or 21 or 20 or 19 or 18 or 17 or 16 or 15 or 14 or 13 or 12 or 11 or 10 or 9 or 8 or 7 or 6 or 5 or 4 or 3 miRNAs that are differentially regulated in blood samples from COPD patients as compared to lung cancer patients wherein the set of miRNAs comprises at least one, e.g. 1, 2, 3, 4, 5 or 6, of the sets of miRNAs listed in FIG. 10.

In a third aspect, the invention relates to the use of set of polynucleotides according to the second aspect of the invention for diagnosing and/or prognosing COPD in a subject.

In a fourth aspect, the invention relates to a set of at least two primer pairs for determining the expression level of a predetermined set of miRNAs in a body fluid sample of a subject suffering or suspected of suffering from COPD.

It is preferred that the body fluid sample is a blood sample, particularly preferred it is a whole blood, PBMC, serum or plasma sample, more particularly preferred it is a whole blood sample.

It is preferred that the subject is a mammal including both a human and another mammal, e.g. an animal such as a mouse, a rat, a rabbit, or a monkey. It is particularly preferred that the subject is a human.

In one embodiment of the third aspect of the present invention, the predetermined set of miRNAs comprises miRNAs that are differentially regulated in blood samples from COPD patients as compared to healthy controls.

Preferably, the predetermined set comprising at least two miRNAs that are differentially regulated in blood samples from COPD patients as compared to healthy controls is selected from the set of miRNAs listed in FIG. 3 or 7.

It is preferred that the predetermined set comprising at least two miRNAs that are differentially regulated in blood samples from COPD patients as compared to healthy controls is selected from the sets of miRNAs listed in FIG. 8.

It is preferred that the predetermined set comprising at least two miRNAs that are differentially regulated in blood samples from COPD patients as compared to healthy controls comprises at least one set of miRNAs listed in FIG. 8.

It is preferred that the set of at least two primer pairs for determining the expression level of a predetermined set of miRNAs that are differentially regulated in blood samples from COPD patients as compared to healthy controls are primer pairs that are specific for at least one miRNA listed in FIG. 3 or FIG. 7.

It is preferred that the set of at least two primer pairs for determining the expression level of a predetermined set of miRNAs that are differentially regulated in blood samples from COPD patients as compared to healthy controls are primer pairs that are specific for at least one set of miRNAs listed in FIG. 8.

It is preferred that the set of at least two primer pairs of the present invention are for detecting a set comprising, essentially consisting of, or consisting of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 40 or more miRNAs, and wherein the set of miRNAs comprises at least one of the sets listed in FIG. 8.

For example, the set of at least two primer pairs of the present invention are for detecting a predetermined set of 40 or 39 or 38 or 37 or 36 or 35 or 34 or 33 or 32 or 31 or 30 or 29 or 28 or 27 or 26 or 25 or 24 or 23 or 22 or 21 or 20 or 19 or 18 or 17 or 16 or 15 or 14 or 13 or 12 or 11 or 10 or 9 or 8 or 7 or 6 or 5 or 4 or 3 or 2 miRNAs wherein the predetermined set of miRNAs that are differentially regulated in blood samples from COPD patients as compared to healthy controls comprises at least one of the sets of miRNAs listed in FIG. 8.

Preferably, the said primer pairs may be used for amplifying cDNA transcripts of the predetermined set of miRNAs that are differentially regulated in blood samples from COPD patients as compared to healthy controls selected from the miRNAs listed in FIG. 3 or FIG. 7. Furthermore, the said primer pairs may be used for amplifying cDNA transcripts of the set of miRNAs listed in FIG. 8.

It is understood that the primer pairs for detecting a predetermined set of miRNAs may consist of specific and or non-specific primers. Additionally, the set of primer pairs may be complemented by other substances or reagents (e.g. buffers, enzymes, dye, labelled probes) known to the skilled in the art for conducting real time polymerase chain reaction (RT-PCR).

In a second embodiment of the third aspect of the present invention, the predetermined set of miRNAs comprises miRNAs that are differentially regulated in blood samples from COPD patients as compared to lung cancer patients.

Preferably, the predetermined set comprising at least two miRNAs that are differentially regulated in blood samples from COPD patients as compared to to lung cancer patients is selected from the set of miRNAs listed in FIG. 5 or FIG. 9.

It is preferred that the predetermined set comprising at least two miRNAs that are differentially regulated in blood samples from COPD patients as compared to to lung cancer patients is selected from the sets of miRNAs listed in FIG. 10.

It is preferred that the predetermined set comprising at least two miRNAs that are differentially regulated in blood samples from COPD patients as compared to to lung cancer patients comprises at least one set of miRNAs listed in FIG. 10.

It is preferred that the set of at least two primer pairs for determining the expression level of a predetermined set of miRNAs that are differentially regulated in blood samples from COPD patients as compared to to lung cancer patients are primer pairs that are specific for at least one miRNA listed in FIG. 5 or FIG. 9.

It is preferred that the set of at least two primer pairs for determining the expression level of a predetermined set of miRNAs that are differentially regulated in blood samples from COPD patients as compared to to lung cancer patients are primer pairs that are specific for at least one set of miRNAs listed in FIG. 10.

It is preferred that the set of at least two primer pairs of the present invention are for detecting a set comprising, essentially consisting of, or consisting of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 40 or more miRNAs, and wherein the set of miRNAs comprises at least one of the sets listed in FIG. 10.

For example, the set of at least two primer pairs of the present invention are for detecting a predetermined set of 40 or 39 or 38 or 37 or 36 or 35 or 34 or 33 or 32 or 31 or 30 or 29 or 28 or 27 or 26 or 25 or 24 or 23 or 22 or 21 or 20 or 19 or 18 or 17 or 16 or 15 or 14 or 13 or 12 or 11 or 10 or 9 or 8 or 7 or 6 or 5 or 4 or 3 or 2 miRNAs wherein the predetermined set of miRNAs that are differentially regulated in blood samples from COPD patients as compared to to lung cancer patients comprises at least one of the sets of miRNAs listed in FIG. 10.

Preferably, the said primer pairs may be used for amplifying cDNA transcripts of the predetermined set of miRNAs that are differentially regulated in blood samples from COPD patients as compared to to lung cancer patients selected from the miRNAs listed in FIG. 5 or FIG. 9. Furthermore, the said primer pairs may be used for amplifying cDNA transcripts of the set of miRNAs listed in FIG. 10.

It is understood that the primer pairs for detecting a predetermined set of miRNAs may consist of specific and or non-specific primers. Additionally, the set of primer pairs may be complemented by other substances or reagents (e.g. buffers, enzymes, dye, labelled probes) known to the skilled in the art for conducting real time polymerase chain reaction (RT-PCR).

In a fifth aspect, the invention relates to the use of a set of primer pairs according to the fourth aspect of the invention for diagnosing and/or prognosing COPD in a subject.

In a sixth aspect, the invention relates to means for diagnosing and/or prognosing of COPD in a body fluid sample of a subject.

Preferably, the invention relates to means for diagnosing and/or prognosing of COPD in a body fluid sample of a subject comprising

-   -   (i) a set of at least two polynucleotides according to the         second aspect of the invention or     -   (ii) a set of at least two primer pairs according the fourth         aspect of the invention.

It is preferred that the body fluid sample is a blood sample, particularly preferred it is a whole blood, PBMC, serum or plasma sample, more particularly preferred it is a whole blood sample.

It is preferred that the subject is a mammal including both a human and another mammal, e.g. an animal such as a mouse, a rat, a rabbit, or a monkey. It is particularly preferred that the subject is a human.

In a first embodiment of the sixth aspect of the present invention, the set of at least two polynucleotides or the set of at least 2 primer pairs are for detecting a predetermined set comprising at least two miRNAs that are differentially regulated in blood samples from COPD patients as compared to healthy controls in a body fluid sample, e.g. blood sample, from a subject, e.g. patient, human or animal, wherein the set of miRNAs is selected from the miRNAs listed in FIG. 3 or FIG. 7.

It is preferred that the set of at least two polynucleotides or the set of at least 2 primer pairs are for detecting a predetermined set comprising at least two miRNAs that are differentially regulated in blood samples from COPD patients as compared to healthy controls in a body fluid sample, e.g. blood sample, from a subject, e.g. patient, human or animal, wherein the set of miRNAs is selected from the sets of miRNAs listed in FIG. 8.

It is preferred that the set of at least two primer pairs for determining the expression level of a predetermined set of miRNAs that are differentially regulated in blood samples from COPD patients as compared to healthy controls. are primer pairs that are specific for at least two miRNAs selected from the miRNAs listed in FIG. 3 or FIG. 7.

It is preferred that the set of at least two primer pairs for determining the expression level of a set of miRNAs that are differentially regulated in blood samples from COPD patients as compared to healthy controls is selected from primer pairs that are specific for at least one set of miRNAs listed in FIG. 8.

In a second embodiment of the sixth aspect of the present invention, the set of at least two polynucleotides or the set of at least 2 primer pairs are for detecting a predetermined set comprising at least two miRNAs that are differentially regulated in blood samples from COPD patients as compared to lung cancer patients in a body fluid sample, e.g. blood sample, from a subject, e.g. patient, human or animal, wherein the set of miRNAs is selected from the miRNAs listed in FIG. 5 or FIG. 9.

It is preferred that the set of at least two polynucleotides or the set of at least 2 primer pairs are for detecting a predetermined set comprising at least two miRNAs that are differentially regulated in blood samples from COPD patients as compared to lung cancer patients in a body fluid sample, e.g. blood sample, from a subject, e.g. patient, human or animal, wherein the set of miRNAs is selected from the sets of miRNAs listed in FIG. 10.

It is preferred that the set of at least two primer pairs for determining the expression level of a predetermined set of miRNAs that are differentially regulated in blood samples from COPD patients as compared to lung cancer patients is selected from primer pairs that are specific for at least two miRNAs selected from the miRNAs listed in FIG. 5 or FIG. 9.

It is preferred that the set of at least two primer pairs for determining the expression level of a set of miRNAs that are differentially regulated in blood samples from COPD patients as compared to lung cancer patients are primer pairs that are specific for at least one set of miRNAs listed in FIG. 10.

It is also preferred that said means for diagnosing and/or prognosing of COPD comprise, of a set of beads comprising a at least two polynucleotides according to the second aspect of the present invention. It is especially preferred that the beads are employed within a flow cytometer setup for diagnosing and/or prognosing of COPD, e.g. in a LUMINEX system (www.luminexcorp.corn).

In a seventh aspect, the invention relates to a kit for diagnosing and/or prognosing of COPD in a subject.

Preferably, the invention relates to a kit for diagnosing and/or prognosing of COPD comprising

-   -   (i) means for determining an expression profile of a         predetermined set comprising at least two miRNAs representative         for COPD in a body fluid sample from a subject, and     -   (ii) at least one reference.

It is preferred that the body fluid sample is a blood sample, particularly preferred it is a whole blood, PBMC, serum or plasma sample, more particularly preferred it is a whole blood sample.

It is preferred that the subject is a mammal including both a human and another mammal, e.g. an animal such as a mouse, a rat, a rabbit, or a monkey. It is particularly preferred that the subject is a human.

In a first embodiment of the seventh aspect of the present invention, the predetermined set of miRNAs comprises miRNAs that are differentially regulated in blood samples from COPD patients as compared to healthy controls.

In a second embodiment of the seventh aspect of the present invention, the predetermined set of miRNAs comprises miRNAs that are differentially regulated in blood samples from COPD patients as compared to lung cancer patients.

Said means may comprise of at least two polynucleotides according to the second aspect of the present invention, a set of at least 2 primer pairs according to the fourth aspect of the invention; means according to the sixth aspect of the present invention; primers suitable to perform reverse transcriptase reaction and/or real time polymerase chain reaction such as quantitative polymerase chain reaction; and/or means for conducting next generation sequencing.

In an eighth aspect, the invention relates to a predetermined set of miRNAs in a body fluid sample isolated from a subject for diagnosing and/or prognosing of COPD.

It is preferred that the body fluid sample is a blood sample, particularly preferred it is a whole blood, PBMC, serum or plasma sample, more particularly preferred it is a whole blood sample.

It is preferred that the subject is a mammal including both a human and another mammal, e.g. an animal such as a mouse, a rat, a rabbit, or a monkey. It is particularly preferred that the subject is a human.

In a first embodiment of the eighth aspect of the present invention, the predetermined set of miRNAs comprises miRNAs that are differentially regulated in blood samples from COPD patients as compared to healthy controls.

Preferably, the predetermined set comprising at least two miRNAs that are differentially regulated in blood samples from COPD patients as compared to healthy controls is selected from the set of miRNAs listed in FIG. 3 or 7.

It is preferred that the predetermined set comprising at least two miRNAs that are differentially regulated in blood samples from COPD patients as compared to healthy controls is selected from the set of miRNAs listed in FIG. 8.

It is preferred that the predetermined set comprising at least two miRNAs that are differentially regulated in blood samples from COPD patients as compared to healthy controls comprises at least one set of miRNAs listed in FIG. 8.

In a second embodiment of the eighth aspect of the present invention, the predetermined set of miRNAs comprises miRNAs that are differentially regulated in blood samples from COPD patients as compared to lung cancer patients.

Preferably, the predetermined set comprising at least two miRNAs that are differentially regulated in blood samples from COPD patients as compared to lung cancer patients is selected from the set of miRNAs listed in FIG. 5 or 9.

It is preferred that the predetermined set comprising at least two miRNAs that are differentially regulated in blood samples from COPD patients as compared to lung cancer patients is selected from the set of miRNAs listed in FIG. 10.

It is preferred that the predetermined set comprising at least two miRNAs that are differentially regulated in blood samples from COPD patients as compared to lung cancer patients comprises at least one set of miRNAs listed in FIG. 10.

In a ninth aspect, the invention relates to the use of a set of miRNAs according to the eighth aspect of the invention for diagnosing and/or prognosing of COPD in a subject.

The invention will now be illustrated by the following figures and the non-limiting experimental examples.

FIGURES

FIG. 1:

Overview of miRNA sequences published in the miRNA database 14.0 plus additional miRNA sequences.

FIG. 2:

General overview of the method of diagnosing and/or predicting the state of health employing predetermined sets of miRNAs.

FIG. 3:

Overview of all miRNAs that are found to be differentially regulated in blood samples of COPD patients, grouped accordingly to their results in t-tests.

FIG. 4:

COPD patients against healthy controls - classification of: according to t-test with the 30 miRNAs with the lowest p-values (see FIG. 3) lead to an accuracy 89.6% a specificity of 91.7% and a sensitivity of 87.5% red=COPD patients (2=derived from 1 independent sample collection); blue=healthy controls (1,2,3,4,5=derived from 5 independent sample collections)

FIG. 5:

Overview of all miRNAs that are found to be differentially regulated in blood samples between lung cancer and COPD-patients grouped accordingly to their results in t-tests.

FIG. 6:

COPD patients against lung cancer patients—classification of: according to t-test with the 270 miRNAs with the lowest p-values (see FIG. 5) lead to an accuracy 89.6% a specificity of 91.7% and a sensitivity of 91.7% red =COPD patients (2=derived from 1 independent sample collection); blue =lung cancer patients (1=derived from 1 independent sample collection)

FIG. 7:

Overview of miRNAs that are found to be differentially regulated between healthy controls and subjects suffering from COPD. Experimental details: SEQ ID NO: sequence identification number, miRNA: identifier of the miRNA according to miRBase, median g1: median intensity obtained from microarray analysis for healthy controls, median g2: median intensity obtained from microarray analysis for individuals with COPD, qmedian: ratio of median g1/median g2, logqmedian: log of qmedian, ttest_rawp: p-value obtained when applying t-test, ttest_adjp: adjusted p-value in order to reduce false discovery rate by Benjamini-Hochberg adjustment, AUC: Area under the curve, wmw_rawp: p-value obtained when applying wmw-test (Wilcoxon Mann Whitney test), wmw_adjp: adjusted p-value in order to reduce false discovery rate by Benjamini-Hochberg adjustment.

FIG. 8:

Predetermined sets of miRNAs (miRNA signatures SNC-1 to 801) that allow for effective diagnosis and/or prognosis of COPD when differentiating COPD and healthy controls. Experimental details: SEQ ID NO: sequence identification number, miRNA: identifier of the miRNA according to miRBase, Acc=accuracy, Spec=specificity, Sens=sensitivity

FIG. 9:

Overview of miRNAs that are found to be differentially regulated between lung cancer subjects and subjects suffering from COPD. Experimental details: SEQ ID NO: sequence identification number, miRNA: identifier of the miRNA according to miRBase, median g1: median intensity obtained from microarray analysis for lung cancer subjects, median g2: median intensity obtained from microarray analysis for individuals with COPD, qmedian: ratio of median g1/median g2, logqmedian: log of qmedian, ttest_rawp: p-value obtained when applying t-test, ttest_adjp: adjusted p-value in order to reduce false discovery rate by Benjamini-Hochberg adjustment, AUC: Area under the curve, wmw_rawp: p-value obtained when applying wmw-test (Wilcoxon Mann Whitney test), wmw_adjp: adjusted p-value in order to reduce false discovery rate by Benjamini-Hochberg adjustment.

FIG. 10:

Predetermined sets of miRNAs (miRNA signatures SLC-1 to 901) that allow for effective diagnosis and/or prognosis of COPD when differentiating lung cancer and COPD. Experimental details: SEQ ID NO: sequence identification number, miRNA: identifier of the miRNA according to miRBase, Acc=accuracy, Spec=specificity, Sens=sensitivity.

REFERENCES

-   Alvarez-Garcia, I. and E. A. Miska (2005). “MicroRNA functions in     animal development and human disease.” Development 132(21): 4653-62. -   Benjamini, Y. and Y. Hochberg (1995). “Controlling the false     discovery rate: A practical and powerful approach to multiple     testing.” J R Statist Soc B 57: 289-300. -   Bolstad, B. M., R. A. Irizarry, et al. (2003). “A comparison of     normalization methods for high density oligonucleotide array data     based on variance and bias.” Bioinformatics 19(2): 185-93. -   Calin, G. A. and C. M. Croce (2006). “MicroRNA-cancer connection:     the beginning of a new tale.” Cancer Res 66(15): 7390-4. -   Calin, G. A. and C. M. Croce (2006). “MicroRNA signatures in human     cancers.” Nat Rev Cancer 6(11): 857-66. -   Chen, X., Y. Ba, et al. (2008). “Characterization of microRNAs in     serum: a novel class of biomarkers for diagnosis of cancer and other     diseases.” Cell Res 18(10): 997-1006. -   Crawford, M., E. Brawner, et al. (2008). “MicroRNA-126 inhibits     invasion in non-small cell lung carcinoma cell lines.” Biochem     Biophys Res Commun 373(4): 607-12. -   Esquela-Kerscher, A. and F. J. Slack (2006). “Oncomirs - microRNAs     with a role in cancer.” Nat Rev Cancer 6(4): 259-69. -   Feitelson, M. A. and J. Lee (2007). “Hepatitis B virus integration,     fragile sites, and hepatocarcinogenesis.” Cancer Lett 252(2):     157-70. -   Gilad, S., E. Meiri, et al. (2008). “Serum microRNAs are promising     novel biomarkers.” PLoS ONE 3(9): e3148. -   Griffiths-Jones, S., R. J. Grocock, et al. (2006). “miRBase:     microRNA sequences, targets and gene nomenclature.” Nucleic Acids     Res 34(Database issue): D140-4. -   Griffiths-Jones, S., S. Moxon, et al. (2005). “Rfam: annotating     non-coding RNAs in complete genomes.” Nucleic Acids Res 33(Database     issue): D121-4. -   Griffiths-Jones, S., H. K. Saini, et al. (2008). “miRBase: tools for     microRNA genomics.” Nucleic Acids Res 36(Database issue): D154-8. -   Guo, L., Z. X. Huang, et al. (2008). “Differential Expression     Profiles of microRNAs in NIH3T3 Cells in Response to UVB     Irradiation.” Photochem Photobiol. -   Harris, T. A., M. Yamakuchi, et al. (2008). “MicroRNA-126 regulates     endothelial expression of vascular cell adhesion molecule 1.” Proc     Natl Acad Sci U S A 105(5): 1516-21. -   Hayashita, Y., H. Osada, et al. (2005). “A polycistronic microRNA     cluster, miR-17-92, is overexpressed in human lung cancers and     enhances cell proliferation.” Cancer Res 65(21): 9628-32. -   He, L., J. M. Thomson, et al. (2005). “A microRNA polycistron as a     potential human oncogene.” Nature 435(7043): 828-33. -   Henschke, C. I. and D. F. Yankelevitz (2008). “CT screening for lung     cancer: update 2007.” Oncologist 13(1): 65-78. -   Hochberg, Y. (1988). “A sharper bonferroni procedure for multiple     tests of significance.” Biometrica 75: 185-193. -   Ichimi, T., H. Enokida, et al. (2009). “Identification of novel     microRNA targets based on microRNA signatures in bladder cancer.”     Int J Cancer. -   Jemal, A., R. Siegel, et al. (2008). “Cancer statistics, 2008.” CA     Cancer J Clin 58(2): 71-96. -   Johnson, S. M., H. Grosshans, et al. (2005). “RAS is regulated by     the let-7 microRNA family.” Cell 120(5): 635-47. -   Keller, A., N. Ludwig, et al. (2006). “A minimally invasive multiple     marker approach allows highly efficient detection of meningioma     tumours.” BMC Bioinformatics 7: 539. -   Lee, R. C., R. L. Feinbaum, et al. (1993). “The C. elegans     heterochronic gene lin-4 encodes small RNAs with antisense     complementarity to lin-14.” Cell 75(5): 843-54. -   Lu, J., G. Getz, et al. (2005). “MicroRNA expression profiles     classify human cancers.” Nature 435(7043): 834-8. -   Mann, H. and F. Wilcoxon (1947). “On a test whether one of two     random variables is stochastically larger than the other.” Ann Mat     Stat 18: 50-60. -   Sassen, S., E. A. Miska, et al. (2008). “MicroRNA: implications for     cancer.” Virchows Arch 452(1): 1-10. -   Scott, W. J., J. Howington, et al. (2007). “Treatment of non-small     cell lung cancer stage I and stage II: ACCP evidence-based clinical     practice guidelines (2nd edition).” Chest 132(3 Suppl): 234S-242S. -   Shannon, C. (1984). “A mathematical theory of communication.” The     Bell System Technical Journal 27: 623-656. -   Stahlhut Espinosa, C. E. and F. J. Slack (2006). “The role of     microRNAs in cancer.” Yale J Biol Med 79(3-4): 131-40. -   Takamizawa, J., H. Konishi, et al. (2004). “Reduced expression of     the let-7 microRNAs in human lung cancers in association with     shortened postoperative survival.” Cancer Res 64(11): 3753-6. -   Team, R. D. C. (2008). R: A Language and Environment for Statistical     Computing. Vienna, Austria, R Foundation for Statistical Computing. -   Tong, A. W. (2006). “Small RNAs and non-small cell lung cancer.”     Curr Mol Med 6(3): 339-49. -   Vapnik, V. (2000). The Nature of Statistical Learning Theory.,     Springer. -   Volinia, S., G. A. Calin, et al. (2006). “A microRNA expression     signature of human solid tumours defines cancer gene targets.” Proc     Natl Acad Sci USA 103(7): 2257-61. -   Vorwerk, S., K. Canter, et al. (2008). “Microfluidic-based enzymatic     on-chip labeling of miRNAs.” N Biotechnol 25(2-3): 142-9. -   Wilcoxon, F. (1945). “Individual comparisons by ranking methods.”     Biometric Bull 1: 80-83. -   Williams, A. E. (2008). “Functional aspects of animal microRNAs.”     Cell Mol Life Sci 65(4): 545-62. -   Yanaihara, N., N. Caplen, et al. (2006). “Unique microRNA molecular     profiles in lung cancer diagnosis and prognosis.” Cancer Cell 9(3):     189-98. -   Zhang, B., X. Pan, et al. (2007). “microRNAs as oncogenes and tumour     suppressors.” Dev Biol 302(1): 1-12. -   Zhang, B., Q. Wang, et al. (2007). “MicroRNAs and their regulatory     roles in animals and plants.” J Cell Physiol 210(2): 279-89. 

1. A method of diagnosing COPD, comprising the steps (a) determining an expression profile of a predetermined set of miRNAs, in a blood sample from a patient, particularly a human patient; and (b) comparing said expression profile to a reference expression profile, wherein the comparison of said determined expression profile to said reference expression profile allows for the diagnosis of COPD.
 2. The method according to claim 1, wherein the predetermined set of miRNAs comprises miRNAs that are differentially regulated in blood samples from COPD patients as compared to lung cancer patients.
 3. The method according to claim 1, wherein the expression profile is determined from miRNAs selected from FIG. 5 or FIG.
 9. 4. The method according to claim 1, wherein the predetermined set of miRNAs comprises at least one set of miRNAs listed in FIG.
 10. 5. The method according to claim 1, wherein the predetermined set of miRNAs comprises miRNAs that are differentially regulated in blood samples from COPD patients as compared to healthy controls.
 6. The method according to claim 1, wherein the expression profile is determined from miRNAs selected from FIG. 3 or FIG.
 7. 7. The method according to claim 1, wherein the predetermined set of miRNAs comprises at least one set of miRNAs listed in FIG.
 8. 8. The method according to claim 1, wherein the predetermined set of miRNAs representative for diagnosis of COPD comprises at least 1, 7, 10, 15, 20, 25, 30, 35, 40, 50, 75, 100 miRNAs.
 9. The method according to claim 1 wherein the expression profile is determined by nucleic acid hybridization, nucleic acid amplification, polymerase extension, sequencing, mass spectroscopy, flow cytometry or any combinations thereof.
 10. A set of polynucleotides for detecting a predetermined set comprising at least two miRNAs for diagnosing and/or prognosing of COPD in a blood sample from a patient, particularly a human patient.
 11. The set of polynucleotides according to claim 10, wherein the miRNAs are selected from the miRNAs that are differentially regulated in blood samples from COPD patients as compared to lung cancer patients listed in FIGS. 5 or
 9. 12. The set of polynucleotides according to claim 10, wherein the predetermined set of miRNAs that are differentially regulated in blood samples from COPD patients as compared to lung cancer patients is selected from the sets of miRNAs listed in FIG.
 10. 13. The set of polynucleotides according to claim 10, wherein the miRNAs are selected from the miRNAs that are differentially regulated in blood samples from COPD patients as compared to healthy controls listed in FIG. 3 or
 7. 14. The set of polynucleotides according to claim 10, wherein the predetermined set of miRNAs that are differentially regulated in blood samples from COPD patients as compared to healthy controls is selected from the sets of miRNAs listed in FIG.
 8. 15. The set of polynucleotides according to claim 10, wherein the predetermined set of miRNAs representative for diagnosis of COPD comprises at least 1, 7, 10, 15, 20, 25, 30, 35, 40, 50, 75, 100 miRNAs.
 16. Use of set of polynucleotides according to claim 10 for diagnosing and/or prognosing COPD in a patient, particularly a human patient.
 17. A set of primer pairs for determining the expression level of a predetermined set of miRNAs in a blood sample of a patient for diagnosing and/or prognosing COPD.
 18. The set of primer pairs according to claim 17, wherein the miRNAs are selected from the miRNAs that are differentially regulated in blood samples from COPD patients as compared to lung cancer patients listed in FIG. 5 or FIG.
 9. 19. The set of primer pairs according to claim 17, wherein the predetermined set of miRNAs comprises at least one set of miRNAs listed in FIG.
 10. 20. The set of primer pairs according to claim 17, wherein the miRNAs are selected from the miRNAs that are differentially regulated in blood samples from COPD patients as compared to healthy controls listed in FIG. 3 or FIG.
 7. 21. The set of primer pairs according to claim 17, wherein the predetermined set of miRNAs that are differentially regulated in blood samples from COPD patients as compared to healthy controls comprises at least one set of miRNAs listed in FIG.
 8. 22. The set of primer pairs according to claim 17, wherein the predetermined set of miRNAs representative for diagnosis of COPD comprises at least 1, 7, 10, 15, 20, 25, 30, 35, 40, 50, 75, 100 miRNAs.
 23. Use of set of primer pairs according to claim 17 for diagnosing and/or prognosing COPD in a patient, particularly a human patient.
 24. Means for diagnosing and/or prognosing of COPD in a blood sample of a subject comprising: (i) a set of at least two polynucleotides according to claim 10 or (ii) a set of at least two primer pairs which determine the expression levels of a predetermined set of miRNAs.
 25. A kit for diagnosing and/or predicting COPD, comprising: (a) means for determining the miRNA expression profile of a RNA sample of a subject, and (b) at least one reference expression profile for a particular condition.
 26. The kit comprising the means according to claim 24 and at least one reference expression profile for a particular condition.
 27. A set of miRNAs isolated from a blood sample from a subject for diagnosing and/or prognosing of COPD, wherein the miRNAs are selected from the miRNAs as indicated in claim
 2. 28. The set of miRNAs of claim 27 bound to a carrier, e.g. a micro-array.
 29. Use of a set of miRNAs according to claim 27 for diagnosing and/or prognosing of COPD in a subject, particularly a human subject.
 30. The method of claim 1, wherein said sample is a whole blood sample. 